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11 Using Technology-Rich Environments to Foster Self-Regulated Learning in Social Studies Eric G. Poitras and Susanne P. Lajoie Introduction The self-regulation of learning in history is an emerging area of research in the field of social studies education. The goal of self-regulated learning (SRL) research is to identify the cognitive, metacognitive, affective, and motivational processes that underlie learners’ efforts to reconstruct the past along with the instructional strategies that can be used to enhance learning and task performance. The challenge facing educators and students in history classrooms is to engage in analytical skills to reason and gain better understanding of the past, drawing inferences on the basis of available and reliable evidence as opposed to memorizing lists of facts and dates (Donovan, Bransford, & Pellegrino, 1999). The seminal works by Wineburg (1991, 1994) highlighted the nature of noviceexpert differences in historical reasoning and documented the complex interplay of cognitive skills involved in evaluating the credibility of historical sources, corroborating information from multiple accounts, and making inferences based on the available evidence and one’s own prior knowledge. Considering the dynamic nature of historical thinking, and how learning unfolds throughout the course of studying multiple and often conflicting or unreliable sources of information, the importance of regulatory processes becomes apparent. From an educational perspective, we consider it essential to situate SRL development and instruction in the context of these disciplinary-based practices. In a review of the terms metacognition, self-regulation, and SRL within contemporary research, Dinsmore, Alexander, and Loughlin (2008) alluded to theoretical frameworks that inform the study of these constructs. Although the definition and measurement of each construct is often inconsistent across studies, researchers typically distinguish between information-processing models (Winne, 2001, 2005; Winne & Hadwin, 1998, 2008, 2010; Winne & Perry, 2000) and social-cognitive models (Pintrich, 2004; Schunk, 2005; Zimmerman, 2001) of self-regulation in academic studying and other forms of learning. SRL emphasizes the dynamic nature of skills, attitudes, motivations, and contextual factors involved in these settings, and attempts to explain individual differences in learning outcomes as well as how to improve learning and performance. For a more detailed discussion of these theoretical frameworks, we refer the reader to Winne (2018/this volume) as well as Usher and Schunk (2018/this volume). This chapter compares the different ways in which SRL has been defined by educational researchers in the area of social studies. In particular, the relationship between self-regulatory processes and historical reasoning is examined under the information-processing view of SRL. To date, there are few studies that have investigated the domain-specificity of SRL (Alexander, Dinsmore, Parkinson, & Winters, 2011). While some research has been carried out on students’ ability to regulate their learning as well as the role of technology in facilitating SRL in the social studies, there is still very little scientific understanding of how these factors mediate students’ understanding of the past. In this chapter, we discuss technology-rich learning environments (TREs) designed to support or scaffold students to gain a deeper understanding of the past that would otherwise be beyond their reach. We critically examine recent empirical evidence on the design of scaffolds in TREs such as hypermedia-based environments and intelligent tutoring systems to promote SRL. In doing so, scaffolds embedded in TREs are progressively faded as students become more competent in regulating certain aspects of their own learning (Lajoie, 2005; Lajoie & Azevedo, 2006). The past two decades has seen rapid developments in scaffolds embedded in TREs designed as metacognitive tools to promote SRL (Azevedo & Aleven, 2013; Moreno & Mayer, 2007; Quintana, Zhang, & Krajcik, 2005), and reviews such as that conducted by Reiser (2004) differentiate between structuring and problematizing certain aspects of learning and task performance. On the one hand, learning tasks can be structured to help students by reducing or sequencing the number of choices to make, providing guidance and cues, and facilitating performance through external representations. On the other hand, tasks can also be made more problematic to challenge students to construct their knowledge by addressing issues or gaps, thereby providing
them with opportunities for meaningful learning to occur. The theoretical assumptions that guide the design of metacognitive tools are described in the next section. Relevant Theoretical Ideas Alexander et al. (2011) reviewed the SRL literature to identify the domains under investigation and whether the choice of domain or task was purposefully defined or manipulated to address a particular research question. Very little research addressed the question of whether SRL may be affected by task or domain differences. The majority of the 77 empirical studies included in the review did not identify any particular academic domain or task and its relevance to specific SRL processes. The most popular disciplines reported in empirical studies on SRL included the areas of science, psychology, and teaching and instruction. An implication of this finding is the possibility that the nature of the task performed in a specific domain such as the social studies, along with the learners’ prior knowledge of disciplinary-based practices, is often overlooked in the research literature. Only in the past five years have studies of SRL directly addressed how disciplinary-based practices in the social studies affect learners’ ability to regulate certain aspects of their own learning. Researchers studying SRL in the social studies have focused on a few particular processes, such as learners’ understanding and level of interest towards a task, as well as how they plan their learning, use strategies, and monitor their progress. The first systematic study of SRL in the social studies was conducted by Greene, Bolick, and Robertson (2010). Learners’ use of SRL processes were assessed in addition to the resulting knowledge of study tactics or strategies used in history as well as gains in declarative knowledge. Historical thinking skills were assessed as the students’ ability to evaluate multiple sources, arrange events in the order of their occurrence, and come to an understanding of how a historical narrative might include multiple perspectives of the same event. The deployment of historical thinking skills was later examined throughout learning and task performance as a form of cognitive operation defined in accordance to information-processing models of SRL (Greene, Bolick, Jackson, Caprino, Oswald, & McVea, 2015; Poitras & Lajoie, 2013). From this perspective, historical thinking is not just a learning product but a process and a topic of cognition whereby we study how students allocate cognitive resources towards monitoring and controlling its products and appraising the amount of efforts required to achieve certain standards. For instance, students might investigate the causes of an historical event by asking appropriate questions, formulating an explanation, and evaluating the trustworthiness of sources while gathering, corroborating, and making sense of conflicting accounts. Another significant aspect of these domain-specific strategies is their interrelationships with students’ understanding of the task and their efforts to set goals, monitor their progress, and adapt their approach in response to changing task conditions. In the following section, we describe the phases of SRL as outlined in the three-phase model of cognitive and metacognitive activities in historical inquiry (CMHI; Poitras & Lajoie, 2013), a model that characterizes disciplinary-based practices in history. Defining the Task Learners consider a learning task based on the resources that are available to them and any relevant constraints that will impact their performance (Winne, 2018/this volume). These considerations may be internal or external to the learner. Internal considerations refer to learners’ knowledge of the historical time period or historiographical practices, as well as their motivation to pursue learning in this area. External factors may refer to the amount of time available to pursue the task, access to more knowledgeable peers or other sources of information, the nature and content of each informational source, and whether or not learners were in control of their learning goals. Historical evidence is often incomplete, unclear, or even contradictory, and the practice of history necessitates learners to continually re-define these task conditions as new evidence emerges, which leads them to re-evaluate their understanding of an event (VanSledright & Límon, 2006). A student learning about the sinking of the RMS Titanic, for instance, might begin by reading a firsthand account of the circumstances surrounding the events of the night of April 14, 1912. Although an iceberg reportedly sank the Titanic, the historical account raises several questions, among which are the reasons why the tragedy occurred.
An inquiry into the causes of the event and one’s own emerging understanding is constrained by the factors mentioned above, and most importantly access to primary and secondary sources to support or refute plausible explanations. These explanations may include, but are not limited to, the high concentration of icebergs due to climate, thermal inversion refracting light and camouflaging the iceberg, the ship’s high travel speed, failure to deliver an ice warning message to the bridge, and the use of wrought iron rivets instead of steel rivets. The writing of an historical narrative is often the end result of the inquiry process, although narratives themselves vary substantially in terms of their audience, structure, and criteria for assessing quality (Voss & Wiley, 2006). Learners’ understanding of the task and the available resources are critical aspects of their efforts to regulate their learning, as they may seek assistance and indicate that some documents are not clear or are unusable. Planning Learners begin to write an historical narrative by stating goals and refining them into sub-goals. During the course of learning, learners may revisit or change their goals when they realize there is insufficient time to meet them or that they need more support to attain their goals. Planning is a complex process where learners progressively define the desired end result of the task, determine what level of achievement is suitable, identify the relevant knowledge that must be gained, and reflect on what steps must be taken to solve a problem (Winne, 2018/this volume). As an example, learners may encounter an important event that occurred in an historical account. However, the account fails to mention any information pertaining to the causes of that event. Learners would likely begin their inquiry into the causes of an event by formulating a tentative explanation, which consists of an argument in favor of a specific cause (e.g., Frederick Fleet, a crewman and lookout, failed to spot the iceberg that sunk the Titanic because he forgot his binoculars). As the learners make progress and use strategies to analyze historical documents, their understanding of the event becomes progressively more coherent. They might consider the role of multiple causes and weigh alternative factors in their explanation of an event (e.g., Frederick Fleet, a crewman and lookout, failed to spot the iceberg due to an optical illusion caused by light refraction) or respond to counterarguments (e.g., binoculars are not effective in the dark) by refuting or acknowledging such factors on the basis of evidence gathered from historical accounts. These different goal-setting and planning activities (i.e., formulating an explanation, weighing alternative causes, and anticipating counter-arguments) are distinguishable in patterns of strategy use and the resulting products or changes made to a narrative. Using Learning Strategies Learners engage in task performance utilizing cognitive operations to make sense of information and transform it in a way that is conducive to their stated objectives. For instance, the information-processing model of SRL assumes several “primitive” operations that include searching, monitoring, assembling, rehearsing, and translating, fittingly labeled as SMART operations (Winne, 2001, 2018/this volume). These primitive operations underlie more complex strategies involved in the transformation of information in a manner conducive to enhancing learning, such as generating inferences or elaborating information based on prior knowledge (Azevedo, Moos, Greene, Winters, & Cromley, 2008; Greene & Azevedo, 2009). In the CMHI model of SRL, learners rely on a range of domain-specific strategies deployed in a cyclical manner during learning while performing inquiries into the causes of historical events. For instance, an argument in favor of a particular cause necessitates that learners gather evidence such as a direct quote taken from a document. In the case of the Titanic’s sinking, historical records may not be easily obtainable or complete, for instance weather records of that time period, survivors’ testimonies, as well as ships’ logs. Consider the following firsthand account provided by Dr. Washington Dodge, an eyewitness of the sinking of the Titanic on April 15, 1912:
The ocean as calm as the waters of a smooth flowing river we rowed off to overtake a boat having a lantern aboard, we being unable to find one in our boat—Having rowed about ¼ mile we found ourselves in close proximity to five boats—We observed the closing incidents the gradual submergence of the ship forward—[…] From this time until shortly after 4 in a sea gradually growing rougher a temperature extremely cold we rowed about—Observing in the darkness what first appeared to be a ship full rigged, but to our disappointment proved to be an ice berg about ½ mile distant. Learners may evaluate a document such as this firsthand account as being a credible source of information by taking into consideration the author’s reasons, perspectives, and opportunity for writing the account. For instance, the carelessness of Dr. Dodge’s own handwriting may be suggestive of his state of mind, writing a firsthand account of the sinking within days after the disaster. The evidence itself may be corroborated by other accounts, which requires the learners to coordinate multiple sources of information. A critical factor in reconciling these different historical accounts is learners’ ability to situate the documents in the time and place of their creation and imagine themselves in the context of the event as it unfolds to understand the values and perspectives of historical figures. This set of historical thinking skills enables learners without any prior knowledge of an event to carefully reconstruct its circumstances and build arguments as to the plausible causes that led to its occurrence. Making Adaptations Adaptations to historical understanding occur when learners evaluate their progress and determine that changes are needed to improve upon certain aspects of learning or task performance. These evaluations are complex and may be the result of (1) judgements about the adequacy of the sources they read in trying to meet their goals, (2) the effectiveness of specific strategies, (3) their emotional responses to certain aspects of the task, (4) their awareness of relevant prior knowledge, and (5) emerging understanding of the topic. These learning experiences leads to re-organizing the retrieval and retention processes that characterize learners’ procedural knowledge, thereby modifying future efforts to perform similar tasks. In particular, adaptations include experiences where learners’ explanations are evaluated against standards of causality. In these evaluations, causality serves as a standard by evaluating whether antecedent events are logically followed by their consequents. In doing so, causal standards constrain learning through the need to organize and interpret information obtained from historical sources as a coherent chain of causes and effects. For instance, learners’ understanding of the causes of the Titanic’s sinking may settle on a primary factor (i.e., the lookout’s failure to quickly spot the iceberg), whereas learners are unaware of the relevant conditions that enabled this event to take place (i.e., atmospheric conditions, high concentration of icebergs) or instead believe in the importance of irrelevant details (i.e., the lack of binoculars). Furthermore, the order of causation is another factor to consider while reflecting on one’s own understanding. For example, is the time delay in reporting the iceberg a necessary condition for the sinking of the Titanic? What if First Officer Murdoch had not ordered a port-around maneuver? Would the Titanic still have collided with the iceberg with the bow veering port? What if steel rather than iron rivets were used? Would the hull have been capable of sustaining the damage? The time delay to report the iceberg may be a necessary condition for the event to occur, but there are also a number of sufficient conditions which are closer to, or immediately responsible for, the outcome. An interesting facet of adaptation is that as an explanation increases in its level of coherence, the nature of the argument shifts from the achievement of lower-order goals (i.e., confirm the most likely cause of an event) to higher-order goals (i.e., weigh alternatives or anticipate counter-arguments). The learners’ inquiries into the causes of the event are performed until coherence in understanding the event is reinstated to a sufficient degree given learners’ motivations, task constraints, and available resources. Learners’ gains in understanding, as a result of the learning task, subsequently impact future inquiries into the topic. For instance, learners may be able to search their memory for relevant information, re-read notes, and infer the motives and beliefs of familiar historical figures. However, learners might also recall how difficult the task was to complete because of the lack of resources, and decide that they should instead pursue their inquiries into other related subjects or consult a
different library. As such, the CMHI model of SRL characterizes the phases and cyclical nature of learners’ ability to regulate certain aspects of their own learning while making inquiries into the causes of events in accordance with disciplinary-based practices in social studies. Having defined what is meant by the domain-specificity of SRL, now we move on to discuss recent empirical findings bearing on these notions and the role of TREs in facilitating SRL. Research Evidence TREs designed as metacognitive tools aim to model, track, and support learners’ self-regulatory processes. In doing so, TREs may scaffold cognitive (e.g., goal-setting, learning strategies), metacognitive (e.g., feeling of knowing, judgement of learning, evaluation of understanding), motivational/affective (e.g., interest, confusion), and behavioral activities (e.g., define task demands, engage in help-seeking behaviors) that mediate learning and task performance (Azevedo, 2005, 2008). There are several reasons why learners may fail to regulate certain aspects of their own learning, e.g., the learning environment lacks structure (Mayer, 2004), or the learners lack the pre-requisite metacognitive knowledge (Veenman, Van Hout-Wolters, & Afflerbach, 2006), or the learners do not understand how to deploy self-regulatory processes during learning (Azevedo & Feyzi-Behnagh, 2011). Over the past decade, there has been an increasing amount of literature on the adaptive capabilities of TREs designed as metacognitive tools. Adaptive TREs provide scaffolds on the basis of the changing needs of each learner, fading assistance as individuals become proficient (Azevedo & Aleven, 2013). The following sections review recent empirical evidence on the role of TREs designed as metacognitive tools to foster SRL in the area of social studies, including hypermedia-based environments and intelligent tutoring systems. Self-Regulated Learning in Hypermedia-Based Environments Hypermedia learning environments enable learners to navigate through multiple sources of instructional content (i.e., text, animations, videos, sounds, and images) in a non-linear manner. The content is made accessible through hyperlinks that are embedded within each document, facilitating learners’ efforts to access and manipulate relevant information (Jonassen, 1996). The most obvious finding to emerge from research on hypermedia is that multiple representations of complex topics may be helpful for learners, allowing them to access, transform, store, and revisit information in a convenient manner (Jacobson & Archodidou, 2000). However, the manner in which these representations are designed and delivered to learners is often problematic. Learners have limited attentional resources that can be allocated to assimilating and retaining information, and poorly designed representations often induce cognitive load (Baddeley, 2001; Mayer & Moreno, 2003). Furthermore, learners’ prior knowledge of the topic or task may not be sufficient to allow them to effectively navigate through the content, resulting in cognitive disorientation due to the large amounts of potentially irrelevant or tangential subject matter (Azevedo & Feyzi-Behnagh, 2011; Lajoie, 2014; Niederhauser, Reynolds, Salmen, & Skolmoski, 2000). In a seminal study on SRL in history, Greene et al. (2010) asked high-school students to perform a think-aloud protocol (TAP; Greene, Deekens, Copeland, & Yu, 2018/this volume) while learning from primary and secondary sources (i.e., five text documents and four images) obtained from a larger collection about the Regulator Movement. The event in question occurred in the 1760s, and involved an uprising of residents in present-day North Carolina who claimed that British colonial officials were engaging in corrupt and oppressive practices. Multiple-choice/true-false tasks and open-ended essays were used to assess prior and gained knowledge of the Regulator Movement that could be attributed to self-regulatory processes. The results confirmed that students demonstrated modest gains in both declarative knowledge and the use of historical thinking skills while writing an essay about the Regulator Movement. The most striking result to emerge from the think-aloud protocol data is that students often failed to engage in planning activities, preferring instead to rely on learning strategies and then monitoring their progress. However, a statistically significant relationship was found between planning activities and learning outcomes. Students who planned their efforts by setting goals and refining them into sub-goals obtained higher scores in declarative
knowledge about the historical topic at the end of the learning session. In other words, these students were better able to recall correctly factual information regarding the event under examination because they set goals during learning. A small but statistically non-significant effect was also observed with regards to learners’ conceptual understanding and use of historical thinking skills while writing an essay. Extensive research has shown that the ability to make inferences and elaborate on information by activating prior knowledge is necessary to improve learning about science topics (see Greene & Azevedo, 2007). However, students were observed to rely most often on ineffective strategies while learning about history (i.e., taking notes, as well as searching and summarizing information or selecting new informational sources). They often mentioned experiencing difficulties while performing the task (e.g., self-questioning as well as negative judgements of learning and evaluation of the usefulness of content). These results are significant in at least two major respects. It may be the case that students would benefit from scaffolds that target goal-setting activities, supporting them to state and reinstate goals on a frequent basis during learning and task performance. Furthermore, students may require extensive training on the use of learning strategies, in particular, elaborative processes that enable students to control their own learning. However, the study conducted by Greene et al. (2010) made no attempt to substantiate claims in regards to the impact of domainrelated differences in SRL processes towards learning outcomes and whether the benefits of elaborative processing may be restricted to science-related topics. The evidence reviewed here seems to suggest an approach to SRL training that may be transferable to multiple domains, including both history and science topics. Greene et al. (2015) conducted a subsequent study where college rather than high-school students were randomly assigned to learn either about the Blue Ridge Parkway (i.e., history topic) or the phase change process as substances move from solids, liquids, and then gaseous states (i.e., science topic). The think-aloud protocol data were examined for the use of fine-grained SRL processes that were found to be predictive of learning gains in each discipline (Greene, Dellinger, Tüysüzoğlu, & Costa, 2013). The method involved the aggregation of effective and ineffective SRL processes into coarse-grained descriptions that characterized the broad phases of SRL (i.e., planning, using strategies, and monitoring) to more accurately model their outcomes towards learning. College students were observed to engage often in strategy use rather than making efforts to monitor or plan certain aspects of their own learning, in a similar manner to the high-school students who learned about the Regulator Movement (Greene et al., 2010; Greene et al., 2015). However, college students did plan their efforts and set subgoals more often than high-school students while learning history, which may have contributed to the increase observed in declarative knowledge gains about the Blue Ridge Parkway. These results supported our previous conclusion that students should be supported in their planning skills while learning from hypermedia. The comparison of effective SRL processes observed while students learn either the history or science topics provides important insights into the design of adaptive hypermedia learning environments. Regardless of the domain of study, students who are able to search their memory for relevant knowledge, reinstate information from memory, elaborate on what was read with prior knowledge, memorize text, as well as monitor the time left to task completion were found to gain more declarative knowledge about the topic than those who enacted other processes. In learning about history rather than the science topic, the ability to guess the content available in a source, monitor the effectiveness of strategies, and engage in self-questioning were positive predictors of scores obtained on post-test declarative knowledge measures. Finally, learning strategies that are exclusive to the history domain, referred to as historical thinking skills (i.e., comparing sources to determine the accuracy of their content and inferring historical figures’ perspectives, thinking, and emotions), are amongst the most underutilized strategies reported by students, but are nonetheless predictive of gains in declarative knowledge. Taken together, these studies support the notion that adaptive hypermedia-based environments should scaffold SRL processes in accordance with disciplinary-based practices. Researchers have noted the importance of demographic characteristics (i.e., high school vs. college level) as well as the domain (i.e., history vs. science) as predictors of students’ self-regulatory processes. In particular, less-experienced students in history require
scaffolds that support them in setting goals and using effective strategies to make sense of information while navigating through hypermedia. The design of scaffolds should also be sensitive to the domain under examination. Learning strategies that are specific to an area may be underutilized by less experienced students due to their lack of domain knowledge. While many SRL processes are transferable across domains, the effectiveness of specific learning strategies may also vary depending on whether students are learning about history or science topics. The problem of fostering SRL processes in accordance with disciplinary-based practices in students with low prior domain knowledge has been examined further in the context of intelligent tutoring environments. Self-Regulated Learning With Intelligent Tutoring Systems Intelligent tutoring systems may be defined as any type of technology that adapts itself to the specific needs of different learners (Anderson, Boyle, Corbett, & Lewis, 1990; Greer & McCalla, 1994; Self, 1999). The adaptive capabilities of such systems are predicated upon comprehensive models and assessments of skills, emotions, and other factors that mediate learning and performance (Desmarais & Baker, 2012; Pavlik, Brawner, Olney, & Mitrovic, 2013). Shute and Zapata-Rivera (2012) listed the main features of intelligent tutoring systems as follows: (1) the capability to capture information about learners using multiple sources of data; (2) the real-time analysis of the data to make inferences about learners’ progress; (3) the selection of the most suitable instructional content or strategy to support learners; (4) the delivery of instruction through the system interface. In doing so, the tutoring system may choose to sequence the order of tasks to be performed by learners or might intervene during task performance through hints, prompts, and feedback (VanLehn, 2006). The MetaHistoReasoning tool was designed to support students in monitoring and controlling their own learning while performing inquiries into the causes of historical events (Poitras, 2015). Students learn about the circumstances in which a particular event occurred, such as the council meeting where the order to deport the Acadians was given by Governor Charles Lawrence at Halifax, in July 1755. However, the narrative excludes any information pertaining to the potential causes or contributing factors that led the governor to make his decision. Learners with low prior knowledge of the domain often fail to notice that gap in their understanding of the event and formulate tentative explanations (see Poitras, Lajoie, & Hong, 2012). The ensuing investigation into the causes of the event requires students to set goals, monitor their progress, and use disciplinary-based strategies to formulate their own explanations based on information obtained from primary and secondary sources. The MetaHistoReasoning tool was conceived as a modular system, wherein the first module completed by the students is designed to support skill acquisition (i.e., Training Module), while the subsequent one enables them to practice and refine these skills (i.e., Inquiry Module). Poitras and Lajoie (2014) asked undergraduate students to learn with the MetaHistoReasoning tool and they examined trace logs of user interactions as well as pre-test measures of domain knowledge and post-test measures of topic knowledge and historical thinking skills. In regards to the pre-test survey, students were unfamiliar with historical practices in general, and did not know a lot about the topic under investigation (i.e., the Acadian Deportation). The examination of the topic knowledge assessments showed students were moderately capable of recognizing statements and inferences drawn from the source documents. The analysis of the least and most elaborate essays written by the students also suggested a broad range of abilities in applying historical thinking skills. On the one hand, the written essays argued in favor of the claim that the deportation was caused by the refusal of the Acadians to swear to the oath of allegiance on the basis of evidence gathered from the historical documents. On the other hand, the most elaborate essay also challenged the neutrality of the Acadians in the conflict with the British government, referring to specific quotations taken from primary sources and questioning the reliability of Governor Charles Lawrence as a witness due to his need to obtain the approval of the board members tasked with the administration of the colony. These findings suggested students were able to apply historical thinking skills such as sourcing and gathering evidence to support either single or multiple causes while formulating an explanation.
The examination of the trace log data of user interactions with the Training Module identified student behaviors that predicted the acquisition of skills deployed while regulating certain aspects of their inquiries into the causes of the event. In the Training Module, students recognized skills shown in examples by choosing amongst eight multiple-choice options. A logistic regression model of the correctness of their responses achieved 76.2% accuracy using a ten-fold stratified cross-validation procedure. The model suggested that historical thinking skills such as gathering, corroborating, and contextualizing evidence as well as using substantive concepts were the most diffi-cult for students to correctly recognize from the examples. Students who spent more time studying and made more attempts to categorize an example were more likely to incorrectly recognize the relevant skills. Furthermore, the amount of prior exposure to examples of a particular skill was found to predict the correctness of example categorizations. In the Inquiry Module, students were expected to practice and refine these historical thinking skills while regulating their own learning about the causes of the Acadian Deportation. A series of decision rules were applied to the trace log data of learner behaviors to detect students’ goal-setting activities and strategy use while performing inquiries into the causes of historical events. The results showed that students performed on average only two lines of inquiries, where an explanation was formulated and revised based on information found by students while analyzing historical sources. The examination of the goals set by students while performing their inquiries suggested that they most often confirmed the most probable cause for the event under investigation, rather than weighing alternative causes or refuting counter-arguments. Furthermore, the strategies used by students to build their arguments also indicated several areas for future improvement, most notably the need to elaborate or describe in full detail the information used as evidence, as well as the need to explain their reasoning or the warrant that linked the evidence to their claim. Furthermore, students should have included additional efforts to build their evidentiary base, which often included repeated mentions of the same factual statement, or facts that were noted as being contradicted by other accounts. These findings suggest that intelligent tutoring systems that adaptively scaffold SRL processes should consider student motivation as well as task conditions when assisting students to use learning strategies in the pursuit of goals. Students pursued only a limited amount of inquiries into the topic and preferred to settle on the most likely explanation for the event under investigation, whether due to lack of time, the amount of evidence available, or low interest in the topic. Such processes prevented students from building a solid evidentiary base; therefore prompts may be needed to support students in describing further the pieces of evidence and warrants of their arguments. The challenge is to design scaffolds that can efficiently support students in acquiring, practicing, and refining the use of these learning strategies, while at the same time allowing them to set meaningful goals to learn about different topics of interest. The following sections of this chapter elaborate further on these notions by making recommendations for future research and suggesting educational implications. Future Research Directions These findings have significant implications for understanding and supporting SRL processes with TREs in social studies education. The scope of this chapter was limited to empirical research that relied on process measures to capture the deployment of SRL processes during learning and task performance. In particular, think-aloud protocols were used to assess learners’ verbalizations of their own thought processes, which provides the basis for researchers to infer the onset of cognitive, metacognitive, behavioral, and affective states that characterize SRL (Greene et al., 2018/this volume). Trace logs data of learner behaviors were also used as observable indicators of SRL processes, because they may be informative of how SRL processes unfold during the course of learning (Bernacki, 2018/this volume). A more systematic approach to these data would identify how events detected through each of these methods are similar or different, and whether the data provide common grounds for supporting theoretical claims. How think-aloud protocol data obtained by Greene et al. (2010, 2015) translate to observable indicators that can be analyzed by computers, and whether these same indicators are validated through alternative approaches, are important issues to explore to establish the validity, reliability, and practicality of assessment methods. These issues notwithstanding, additional research using controlled hypermedia
manipulations (i.e., history and science subject matter), as well as analysis of finer-grained constructs using process measures are needed to better understand the role of disciplinary-based practices within SRL frameworks. One of the most important assumptions underlying the design of scaffolds in TREs is whether SRL processes can be externally supported by human or artificial tutors, as well as tools and representations, in a scalable manner. We use the term scalable to refer to any scaffolds embedded in TREs that support SRL processes while taking into account topic, domain, and/or environmental considerations and are faded once the learners become more competent. Learners are hypothesized to progressively internalize SRL processes, leading them to become more autonomous and proficient in their own future learning. Scalable scaffolding solutions enable TREs to adapt the delivery of instructional content, prompts, and materials depending on the topics studied by learners, the domain under investigation, or other scaffolds made available in the learning environment. As researchers differentiate between SRL processes that are generalizable across domains and others that are situated in specific disciplines such as the social studies, we call on the broader community to establish common standards for scaffold design guidelines that take into consideration the role of these mediating factors. Research into the domain-specificity of SRL is still in its infancy, but it raises important questions about the feasibility of designing scalable scaffolding solutions in TREs, and the challenges involved in replicating research to evaluate their effectiveness in fostering SRL. For example, a textbox designed to support learners in making inferences may be made available under specific experimental conditions (i.e., pre-defined groups of students with access to different hypermedia pages), while logging the written content for further analysis. A research and training platform that facilitates access to these datasets, interface plug-ins, databases of hypermedia content, and standardized log records stands to increase the feasibility of group-randomized experiments of domain-related differences in SRL (Greene et al., 2015), as well as the scalability of models and scaffolds embedded in the system interface (Poitras & Lajoie, 2014). The issue relates to the broader impacts of how these findings translate to improved tool designs that foster the most effective SRL processes in specific tasks and domains, and how to make them widely available to the academic and educational community in a timely manner given the current state of the research literature. Implications for Educational Practice The findings obtained in recent empirical studies on SRL have several practical considerations for social studies education. Students have opportunities to practice how to regulate certain aspects of their own learning when it is necessary to perform a task such as solving a problem or answering a question. Teachers may ask groups of students to investigate questions and report their findings to the rest of the class. The open-ended nature of these tasks demand that students cooperate in terms of setting common goals, using strategies, and monitoring their progress while making sense of information obtained from an online collection of documents on the relevant topic. By problematizing the subject matter and challenging students, they have the opportunity to engage in meaningful problem-solving that results in SRL development. As noted by Greene et al. (2015), an emphasis must be placed on supporting students to set goals and monitor their progress through external representations of such activities. This also provides opportunities for students to engage in disciplinary-based practices while regulating their own learning. In order to support students in acquiring such skills, teachers can explicitly model or verbalize these thought processes by explaining how proficient learners approach a similar task, and the strategies that are helpful in making sense of the past. The teacher can also monitor the performance of each group by offering hints, answering questions, and making recommendations while students are searching for relevant information. As was made evident in the empirical literature, software tools may serve as meta-cognitive tools by creating external representations that structure thought processes in a manner that promotes learning and task performance (Poitras & Lajoie, 2014). The same line of reasoning applies to the classroom environment, and ensuring that students have access to a variety of means to express and record their thinking as it evolves throughout the course
of task performance is crucial to promoting self-assessment and reflective practices. For instance, one of the students in each group may rely on note-taking software or use sticky notes to evaluate what is known about the topic and what is unknown. Another student may keep track of the remaining time and list all the web pages that were visited by the group in order to set the search priorities and persist in finding relevant information. Periodically, teachers may inform each group to review the progress they have made so far, in order for students to re-adjust their efforts and correct mistakes. In this view, students encounter situations that are authentic and meaningful, enabling them to express their own understanding and interests while monitoring their progress using external visualizations. The assumption is that with repeated exposure to such tasks, students internalize effective strategies that are articulated by the teacher, allowing them to be successful and autonomous learners in similar circumstances (Usher & Schunk, 2018/this volume). In conclusion, researchers can rely on TREs to serve as both research and training platforms to model and foster SRL in accordance with disciplinary-based practices. In the social studies, learners monitor and control their emerging understanding of historical events while reading firsthand and secondhand accounts (Poitras & Lajoie, 2013). Furthermore, the evidence suggests that the SRL processes that mediate successful learning may differ across disciplines, such as social studies and sciences. These findings raise intriguing questions regarding the nature of SRL skills and the extent of their transferability across domains, tasks, and the role of prior knowledge regarding disciplinary-based practices. Research infrastructures can facilitate work in this area, raising the possibility of personalized learning solutions that implement scalable models and scaffolds to foster SRL within and across academic disciplines. References Alexander, P. A., Dinsmore, D. L., Parkinson, M. M., & Winters, F. I. (2011). Self-regulated learning in academic domains. In B. Zimmerman & D. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 393–407). New York: Routledge. Anderson, J. R., Boyle, C. F., Corbett, A., & Lewis, M. W. (1990). Cognitive modelling and intelligent tutoring. Artificial Intelligence, 42, 7–49. Azevedo, R. (2005). Computer environments as metacognitive tools for enhancing learning. Educational Psychologist, 40, 193–198. Azevedo, R. (2008). The role of self-regulated learning about science with hypermedia. In D. H. Robinson and G. Schraw (Eds.), Recent innovations in educational technology that facilitate student learning (pp. 127–156). Charlotte, NC: Information Age Publishing Inc. Azevedo, R., & Aleven, V. (2013). Metacognition and learning technologies: An overview of current interdisciplinary research. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 1–16). New York: Springer. Azevedo, R., & Feyzi-Behnagh, R. (2011). Dysregulated learning with advanced learning technologies. Journal of e-Learning and Knowledge Society, 7 (2), 9–18. Azevedo, R., Moos, D. C., Greene, J. A., Winters, F. I., & Cromley, J. G. (2008). Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia? Educational Technology Research and Development, 56 (1), 45–72. Baddeley, A. (2001). Is working memory still working? American Psychologist, 56, 849–864.
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Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531–566). San Diego, CA: Academic Press. Zimmerman, B. J. (2001). Achieving academic excellence: A self-regulatory perspective. In M. Ferrari (Ed.), The pursuit of excellence through education (pp. 85–110). Mahwah, NJ: Erlbaum. 12 Self-Regulated Learning in Music Practice and Performance Gary E. McPherson, Peter Miksza, and Paul Evans Introduction This chapter discusses self-regulated learning (SRL) in the context of music. SRL holds significant potential for increasing the efficiency of musical skill acquisition across all aspects of music performance instruction. We begin with a review of selected research that has studied skill acquisition when learning to play a musical instrument. Although the literature related to this topic is growing steadily, much of the scholarship is scattered and atheoretical. Moreover, researchers in music tend to concentrate on behavior and cognition as separate and somewhat unrelated theoretical topics to the exclusion of affect. We discuss these limitations and present a summary of literature that brings research-based evidence pertaining to behavior, cognition, and affect together into a coherent SRL framework. Current and future research priorities are then detailed as a means of outlining ways of maximizing music practice, teacher-student interactions, and efficient approaches to learning complex musical skills. Our final section summarizes the discussion and provides implications for how SRL might be adopted more widely in the music education domain.1 Relevant Theoretical Ideas Underlying Musical Self-Regulated Learning There are multiple reasons why SRL is relevant to studying music performance. Musicians spend hours practicing their instrument or voice by themselves and rehearsing with others, and unlike many other areas of academic learning, they typically do this because they have made a choice to study music, rather than being required to learn as part of a school curriculum (McPherson, Davidson, & Faulkner, 2012; McPherson & Zimmerman, 2002, 2011). It is self-evident that being able to monitor and control one’s learning is fundamental to the acquisition of the highest level of music performance skill. Yet despite this view, the area of instrumental and vocal performance is one of the most conservative domains of learning. It is dominated by teacher-centered pedagogy and a strong master-apprentice model in which a highly experienced music performer imparts his or her knowledge to a passive, receptive learner (Bennett, 2008). Indeed, there are many music performance teachers who are skeptical of the value of research and who view the processes of acquiring artistic expertise as too disparate or esoteric to study. Consequently, music teaching and learning processes are often characterized by a hierarchical and asymmetric pattern of interaction that can leave little room for students and teachers to discuss and reflect on the process, and few opportunities for input from the students (Creech & Gaunt, 2013; Young, Burwell, & Pickup, 2003). Such pedagogy compromises motivation and stifles practice quality, yet in many music environments, particularly at the tertiary levels, such approaches have remained largely unchanged and unchallenged for centuries. A major distinction in music is that musicians do much of their personal skill development practicing in isolation, as compared to other areas such as sport, dance, and theater, where a coach or director provides informative feedback and well-defined tasks during frequent rehearsals or training sessions. In one-to-one studio sessions, teachers tend to focus on music techniques and interpretation of repertoire, rather than informative feedback regarding the student’s hierarchy of goals, the strategies they are using, and how to monitor their progress towards goals. As a result, students do not explicitly learn how to practice. Accordingly, the practice quality of typical
music performance students is often poor because of their use of ineffective and unsystematic strategies. It is for this reason that we believe that SRL provides the best theoretical and applicable framework for understanding the context-specific set of processes that learners draw upon as they promote their own learning. Yet another challenge in the music literature is the dominance of behavioral approaches to studying expert performance as a means of understanding the psychological mechanisms underpinning high-level performance and using this knowledge to inform the training of less advanced learners. To date, much of the literature on music practice has focused on either tracking the behaviors of musicians while practicing (Gruson, 1988, Gabrielsson, 1999; Miksza, 2006a; 2011b, Palmer, 2013) and the quantity of deliberate practice they engage in while practicing (Ericsson, Krampe, & Tesch-Römer 1993). Initial theoretical efforts to apply the SRL framework examined the extent to which young music learners displayed the capacity to engage in the kinds of SRL processes that unsupervised home practice demands (McPherson & Zimmerman, 2002, 2011; McPherson & Renwick 2011). The first author framed much of these initial approaches to research in this area around Zimmerman’s (2000) six socialization processes that collectively explain a developmental pathway that beginning musicians experience as they progress during the early years of their learning. As can be seen in Table 12.1, these six dimensions—motive, method, time, behavior, physical environment, and social processes—emphasized the kinds of support that young learners need from others before they can be internally regulated themselves. Like the measurement of any artistic competence, assessing musical development and achievement is complex. Our early thinking about learners’ progress during the initial years of their instrumental lessons therefore focused on questions such as: What type of performance skills did the learners acquire and what differences distinguished successful versus unsuccessful learners? What were the learners thinking when they were performing music? What metacognitive strategies did they adopt to monitor their own performance and practice? We were also interested to understand how often the beginners needed to be supervised or encouraged to practice, and as they matured, the degree to which they became less dependent on others to regulate their practice. Table 12.1 Dimensions of musical self-regulation. Reprinted with permission from McPherson & Zimmerman (2002,2011) Research Evidence in Music The theoretical ideas outlined above focused on the attributes music learners need to acquire in order to plan, monitor, direct, and regulate their own learning when practicing their instrument or voice by themselves. Research emanating from these approaches has helped to better understand how contemporary music teaching might be updated and redefined through the adoption of SRL. In this section, we review a range of our findings on SRL processes in instrumental music learning. Research on Beginning Instrumentalists in Childhood and Adolescence The first author’s empirical work on music-related SRL processes began with studying beginning instrumentalists in a range of different school music programs. This work traced children who began their instrumental learning around the ages of 8 and 9 and continued for another fourteen years until all had left high school and many had
completed university. Along the way, many of the children had ceased learning music but as they reflected back on their experience the impact of this learning provided valuable insights into their thoughts and feelings about having been a part of an elective instrumental program, and the value it played in their overall educational development. From Other- to Self-Regulated Music Learner As shown in Table 12.1, a key aspect of the research examined how the children’s musical development followed a simple linear trajectory of moving from other- to self-regulation as indicated by the initial support the children received from their teachers and parents. Our early estimates of the importance of other-regulation focused on the children’s home practice, yet as we examined the data, we found some puzzling results. For example, we expected that children who continued learning would be those who were the most consistent and regular with their practice, which we defined at that time as the most self-regulated learners. However, what we found was that children who continued into the second year did not typically practice at the same time every day (McPherson & Davidson, 2002)—a habit that is often anecdotally positively associated with a more conscientious approach to instrumental practice. Our closer examination of the data showed that those children who practiced at the same time of the day were often doing so because they were receiving high levels of reminders and external encouragement or even coercion from their teachers or parents, as compared to initiating the practice by themselves as a result of their own internalized self-regulation. This finding was supported by further evidence showing that the children whose parents used rewards to incentivize practice gave up earlier than their peers whose parents did not use such rewards (Faulkner, Davidson, & McPherson, 2010). Consistent with other studies on instrumental learning (Davidson, Howe, Moore, & Sloboda, 1996), differing kinds of parental support, such as gentle reminders, supportive comments, and informal checking of whether the child practiced his or her instrument each day, were seen as positively supporting the child’s sense of autonomy. These could be compared to the more extrinsic motivators such as rewards or controlling and demanding interactions that apparently diminished the child’s interest in learning an instrument (McPherson & Davidson, 2002; McPherson, 2009). As we tracked children through their first year of learning, we observed changes in parents’ support for music practice. Whereas many parents will continue to remind their children to complete their homework over many years, the support our learners received from their parents tended to decrease toward the end of the children’s first year of learning (McPherson & Davidson, 2002; McPherson, 2009). Unfortunately, this is the very time the children needed ongoing encouragement to continue across the difficult period of adjusting to their instrument and gaining sufficient skill to continue into their second year of learning. We found also that some of their parents began to form judgments about their child’s ability to cope with practice, as well as their own capacity to devote energy into regulating the child’s practice through continual reminders and encouragement. Many parents therefore tended to withdraw their reminders, often because they felt that their child did not have a flair for learning music, was not investing the effort and commitment required, or because the parents were unwilling to invest their own personal time and effort into regulating their child’s daily schedule. In other words, some of the parents had given up on their children much sooner that the children had given up on themselves (McPherson & Davidson, 2002; McPherson, 2009). Task Strategies and Practice Behaviors One initial attempt to understand SRL processes in music practice focused on the observable behaviors of beginning and intermediate level children from the early months of learning until three years later (McPherson & Renwick, 2001). The videos from this study of children practicing their instrument at home were analyzed according to practice content, the nature of performance errors and off-task behaviors, and interactions of family members. These early data collection techniques were important because they clearly documented low levels of self-regulatory behavior as evidenced in the children’s lack of effective practice behaviors. Most of the students’
practice time was spent playing through pieces once or twice, with most errors being ignored or corrected by repeating one or two notes. Importantly, the self-regulatory processes used by the children varied widely thus providing important clues on why some music learners develop their performance skills quickly while others struggled. As part of this longitudinal study, McPherson (2005) administered music performance tests at the end of each school year, across the first three years of their learning, to assess the beginning learner’s abilities to perform rehearsed music, sight-read, play from memory, play by ear, and improvise. Ongoing interviews were also undertaken with the children’s mothers in order to calculate how much practice they had accumulated on their instrument. During the process of assessing the music performance skills, the children were asked a series of questions that helped to identify the quality of task-related strategies they adopted to perform in each of the five ways. The results of this study showed that understanding children’s musical progress involves much more than simply examining the relationship between the amount of practice they have accumulated and their achievement on their instrument (McPherson, 2005; McPherson, Davidson, & Evans, 2016). Watching the children develop across the three years and analyzing their responses provided ample evidence that better players possessed more sophisticated mental strategies for playing their instrument very early in their development and that these players were the ones who went on to achieve at the highest level. Importantly, these were the most self-regulated players who knew when and how to apply their strategies (especially when asked to complete the more challenging musical tasks), possessed the general understanding that their performance was tied to the quality of their effort (particularly effort expended in employing appropriate strategies to complete individual tasks), and were able to coordinate these actions to control their own playing. Motivation and SRL in Music Learning Yet another aspect of research on this group of learners sought to understand why some students are more selfregulated than others. In one case study, the practice sessions of a 12-year-old clarinetist revealed stark differences between the intensity of her practice of repertoire assigned by her teacher as compared to a piece that she asked her teacher to learn (Renwick & McPherson, 2002; McPherson & Renwick, 2011). While practicing the piece assigned by her teacher, this student spent on average one second per note, almost exclusively using her ‘default’ play-through approach without stopping to work on errors. In contrast, while practicing a piece she had chosen herself, her time per note increased eleven-fold, and she was observed adopting more self-regulated, strategic behaviors, such as silently replicating the physical movements needed to play through passages, thinking carefully about upcoming measures before attempting to play them, deliberately slowing the music to grasp the actions necessary to play it, repeating sections, and stitching together short sections into longer, coherent sections. In this case, the student’s more sophisticated practice of repertoire she chose was based on her desire to improve her playing and her determination to fully master the piece she wanted to learn. This result is consistent with findings showing that providing choice about what to work on and which method to use will increase intrinsic motivation and task involvement (Evans, 2015; McPherson, Davidson, & Evans, 2016). The motivational climate seems to be an important antecedent for SRL and ongoing engagement with music. Students from homes where music was valued and seen as meaningful were more likely to value their music education and set higher musical expectations for themselves (McPherson & Davidson, 2002). In our longitudinal study, we studied the impact of school cultures that valued active, quality music programs, and found that over a ten-year period, students in these schools persisted with their music learning much longer before giving up (Evans & McPherson, 2015). In parallel with this longitudinal study, work with other groups of music learners has sought to understand the importance of self-efficacy beliefs in demanding music performance situations. Music learners who were preparing for externally graded music performance examinations were surveyed before they undertook their performance examination. Structural equation modeling of two different populations showed that self-efficacy
was a strong predictor of the student’s performance result in the examination, even more so than the amount of formal practice students devoted to preparing for their examination (McCormick & McPherson, 2003; McPherson & McCormick, 2006). Research With Intermediate and Advanced Musicians The research by McPherson and Evans within the Australian environment with children and adolescents has run in parallel to research in the United States by Miksza (2011a) focusing on more advanced students in high school and college. Miksza and colleagues have conducted observational studies of advanced (Miksza, 2006a, 2011b) and intermediate musicians practicing (Miksza, 2007; Miksza, Prichard, & Sorbo, 2012), as well as questionnairebased studies investigating intermediate (Miksza, 2006b, 2011c) and advanced musicians’ motivational dispositions and self-regulatory practice habits (Ersozlu & Miksza, 2015; Miksza & Tan, 2015). The work with intermediate musicians corroborates that of McPherson and colleagues in that beginning instrumentalists often reported or were often observed practicing with a lack of planning, direction, and self-monitoring. In contrast, the research dealing with more advanced musicians helped to identify the types of practice strategies (e.g., slowing, chaining, whole-part-whole playing) and motivation orientations (e.g., mastery and approach success) that seem to be most consistently associated with performance achievement and other indicators of self-regulation. Miksza (2015) tested the effect of self-regulation instruction among advanced, collegiate instrumentalists. Participants were randomly assigned to a group that included video-based instruction in either (a) the application of practice strategies (slowing, repetition, whole-part-whole, chaining), or (b) self-regulation principles (concentration, goal-selection, planning, self-evaluation, rest/reflective activity) in addition to the aforementioned strategies. Pre- and post-test measures of performance achievement, self-efficacy, and practice behaviors were taken. The musicians’ who received the self-regulation instruction showed greater gains in performance than those who did not. No significant differences were found between the groups’ practice behaviors or self-efficacy reports. However, there was a fairly clear trend in the data suggesting that those who received the self-regulation instruction tended to feel more efficacious at the end of the study. Most recently, Miksza, Blackwell, Roseth, and Cole (2016) examined the effectiveness of a pedagogical approach for enhancing advanced, collegiate music students’ SRL tendencies by using a multiple-baseline experimental design with an intervention staggered across three participants. The intervention emphasized learning processes emblematic of the forethought, performance, and self-reflection phases described in Zimmerman’s (2000) SRL process (Usher & Schunk, 2018/this volume). The intervention consisted of instruction in adaptive behavioral strategies and positive reframing of attitudinal beliefs. The behavioral strategies emphasized elements of practice highlighted in Miksza’s (2006a, 2007, 2011b) previous work such as goal setting, repetition techniques, matching strategies to particular objectives, mental imagery, and mindful attention to focused activity. Attitudinal topics included degree of intrinsic interest for and value of practicing, personal appraisals of one’s efforts, implicit theories of ability, response patterns when confronted with failure, attributions for success, and achievement goal orientations. The results of this study indicate that the intervention seemed to help the students develop a more sophisticated understanding of how to set goals, plan for practicing, and execute strategies in a deliberate manner. Overall, the participants were very forthcoming about how the personalized intervention increased their awareness of just how much care can be applied to developing a self-regulated practice approach. Given the research design employed, these findings cannot be generalized and must be considered tentative. Conducting research with more rigorous, true-experimental designs would help to assess whether interventions of these sort could lead to robust effects. Autonomy Supportive Learning Environments Autonomy has been examined as an important motivational antecedent to the use of SRL strategies in music (Evans, 2015). Autonomy is central in a young music learner’s musical development where the social context plays an important role in development and where teacher’s autonomy support is associated with positive
outcomes in learners. Studies with certified Suzuki-trained teachers (Küpers, van Dijk, van Geert, & McPherson, 2015) have sought to explore what types of teacher-student interactions best foster autonomy supportive learning and might best lead to higher engagement by the beginning music learner. ‘Engagement’ in this context may be considered as a conceptualization of SRL, as it was operationalized in this study as behavioral involvement in the lesson, help-seeking through asking the teacher questions and verbally interacting with the teacher, and showing initiative and creativity in the lesson. Student states in string instrumental lessons were coded according to levels of autonomy and engagement. The relationship between autonomy and engagement was configured using a fourquadrant representation as shown in Figure 12.1: 1. Autonomous engagement (high autonomy and displaying SRL). Used to describe instances during the lesson where the student would take on-task initiative by asking questions or making relevant on-task remarks. 2. Resistance (high autonomy and negatively engaged). Used to describe instances during the lesson when students were actively resisting the task, by saying, for example, ‘I don’t want to do this,’ or by making off-task remarks such as ‘I’m going to the playground later.’ 3. Mimicry (low autonomy and positively engaged). Used to describe instances during the lessons when student would be ‘going with the flow,’ merely doing what was expected by the teacher and not displaying their own SRL. 4. Absence (low autonomy and negatively engaged). Used to describe instances during the lesson when the student was not engaged with the task, would not answer teacher questions, but also not be actively resisting what was being taught. (see Küpers, van Dijk, van Geert, & McPherson, 2015, pp. 340–341) Using the above framework resulted in the observation of large differences in the dyadic transactions between students higher in autonomy and those lower in autonomy. We also observed instances where autonomy support was ‘negotiated’ between teacher and student, where the autonomy support and autonomy expression levels of both the teacher and student were well coordinated, and moments where there were large discrepancies. We describe this relationship as co-regulation (see also Hadwin, Järvelä, & Miller, 2018/this volume). These results led to an exploration of the function of the dyadic synchrony in autonomy levels and the conclusion that maintaining momentum in the learning process depends in part on interactions where a teacher offers autonomy support at a higher level than the student’s current level of autonomy. A key point is that there is no single approach that will work for every learner, and that autonomy development and the techniques teachers use to provider autonomy support need to be tailored to each individual student. Further research will also need to understand more fully the moment-to-moment synchronicity in teacher-student interactions and how moderate amounts of asynchrony might actually challenge, stimulate, and propel a student to higher levels of engagement.
Figure 12.1 Four quadrants of student states of autonomy (Reprinted with permission from Küpers, van Dijk, van Geert, & McPherson, 2015, p. 341) Future Research Directions The streams of work reported above in Australia and the United States are now coming together as part of a program of research that connects SRL with self-determination theory (Ryan & Deci, 2000; Evans, 2015). The research agenda examines motivation and practice quality in musicians who are majoring as performers in undergraduate music degrees and is focused on the types of research issues described below. Our conceptual approach attempts to explain the complex relationships between motivation (operationalized as self-determined motivation), practice quality (operationalized as SRL), and performance outcomes as they unfold over time. The innovative aspect of this approach is that we are using self-determination theory to study how musicians’ psychological needs and feelings of competence, relatedness, and autonomy impact their overall levels of motivation to become competent in music in combination with SRL. The self-determination theory framework can help explain how particular aspects of the music-learning environment can be problematic for learners: competition, anxiety, poorly-defined personal goals, and the teacher-centered approach to instruction so typical of the music studio environment at these levels. Evans and Bonneville-Roussy (2016), for example, found that university music students with self-determined motivation reported better, more productive practice. BonnevilleRoussy and Bouffard (2014) also found that when practice quality (self-regulated practice) was taken into account, it predicted achievement much better than considering the amount of weekly practice alone. It seems feasible therefore that practice quality—operationalized as musical SRL—may be predicted by self-determined motivation. Another advantage of the SRL framework is that it covers the cognitive, affective, and behavioral processes involved when musicians plan their practice, approach difficult or novel tasks, master new repertoire and techniques, and reflect on their progress. Previous research on music practice has tended to focus either on cognitive, behavioral, or affective aspects of practice, without accounting for all three dimensions together. For example, a typical research strategy is to observe and then record the accuracy of the practice or map out how musicians document and reflect on their individual practice using self-report questionnaires, interviews, and journals of practice over time. But the limitation of this is that it does not have the theoretical and conceptual framework that SRL offers to be able to understand why particular strategies may or may not be effective. One technique we are using to approach these issues is microanalysis (see further Cleary & Callan, 2018/this volume). We are in the process of applying SRL microanalysis to gain an understanding of the cognitive, affective, and behavioral processes involved when students practice their instrument or voice. Microanalysis is “a strategic, coordinated plan of administering context-specific questions targeting multiple cyclical phase sub processes as students engage in authentic activities” (Cleary, Callan, & Zimmerman, 2012, p. 4). As such, it is a powerful way to examine SRL because it targets the full range of SRL processes and avoids the limitations of retrospective selfreport measures that regard SRL as a generic, rather than context-specific, set of processes. Our approach to microanalysis involves asking questions immediately before musicians commence a practice session, and then replaying a video of the practice session which the musician has just completed and asking brief context-specific questions for them to explain what they were doing, thinking, or feeling. The videos are used to avoid intruding on the lesson and manipulating the particular SRL strategies used by the leaners. The use of videos in this way reflects a kind of stimulated-recall methodology (Calderhead, 1981), where video or audio is used to allow participants to ‘relive’ certain aspects of their experiences. The method thus maximizes the benefit of ecological validity by not intruding in the learning process itself, but somewhat overcomes the limitations of retrospective recall by providing a rich stimulus for more accurate recall. Videos of the microanalysis are also analyzed and coded according to the fore-thought, performance, and reflection cycles that occur during the practice sessions. This work (McPherson, Osborne, Evans, & Miksza, submitted) aims to extend our previous
studies that have attempted to understand the self-regulatory processes of beginning and intermediate level musicians. Self-Regulated Learning Interventions in Music Yet another area of our collaboration will utilize intervention studies to examine whether explicit instruction in motivation and practice quality can deliver lasting changes across time. Specifically, we are interested in examining whether targeted instruction can have a lasting effect on student motivation and practice quality. Part of the motive for undertaking this type of research is to help music teaching evolve from the longstanding conservative master-apprentice tradition that pervades much oneto-one instrumental and vocal teaching. Our future work therefore seeks to determine ways of introducing concepts and better teaching practices to musicians who teach in university music schools. Education-based research interventions show that teachers appear to be able to effectively learn how to adapt to a student-centered environment, and provide more autonomy-supportive environments for their students, even when they may otherwise be resistant to such ideas (Su & Reeve, 2010). Our own anecdotal experience with students undertaking university pedagogy courses shows that trainee instrumental and vocal teachers are quick to accept student-centered environments, even though the master teachers with whom they are taking music lessons appear to be far less willing to change their teaching strategies. The same may apply for the explicit teaching of SRL strategies. Miksza, McPherson, Herceg, and Meider (in press) have written specifically on the benefits of applying SRL theory to beginning and intermediate levels of instrumental music pedagogy. This work represents a collaboration between researchers and current school music teachers and includes descriptions of two exemplar music lessons for systematically introducing and reinforcing SRL principles among developing musicians. In both cases, the teachers describe methods that encourage the students to become autonomous learners via clear sequencing, peerbased activities, reflective exercises, and care for developing efficacious and adaptive motivation dispositions. Overall, our core research aim is to understand how motivation and practice quality facilitate the acquisition of expert music performance skills. Our current belief is that there is a cyclical relationship between the two and that in music learning influences will occur in both directions where self-determined motivation is likely to facilitate self-regulatory strategies being spontaneously implemented, and in turn, self-regulatory strategies will fulfill basic psychological needs and facilitate self-determined motivation. In a study of a mathematics classroom, for example, students who were taught only self-regulatory skills showed declines in achievement, but those who were taught self-regulatory skills plus motivational strategies improved their achievement (Blackwell, Trzesniewski, & Dweck, 2007). Building on from our previous research, the proposed project will be the first to investigate these possibilities in the domain of music. Conclusions Surveyed within this chapter are some recent theoretical developments in SRL as applied to learning a musical instrument or voice. In the earlier sections we discussed research by the authors that has applied SRL theory. Subsequent sections documented (a) our current research which attempts to encourage innovation through the adoption of SRL theory, and (b) our more recent and ongoing collaborative efforts to combine this with selfdetermination theory to better understand music practice quality and motivation. Research that clarifies more precisely how students develop into self-regulated musicians deserves special attention from music scholars who wish to draw upon and integrate some of the major developments in education into their own research. Adapting and expanding current theories on this issue and drawing on and integrating information from other areas of educational psychology will enable music researchers to develop more sophisticated theories of musical development that can be used to underpin future teaching and learning in music.
Most areas of education are slow to adapt to changing views that are based on research evidence, and this is particularly prominent in music education. Only when evidence-based approaches are sufficiently developed and presented to musicians and music educators in ways they can easily understand will the profession be convinced of the benefits for adopting different techniques for engaging learners and encouraging them to take charge of their own music learning in a self-regulated fashion. As part of this transition, it will undoubtedly be challenging to move from teacher-dominated methods of instruction where the ‘master teacher’ is seen as the most important source of knowledge, and where skill acquisition through repeated, habitual practice is the norm, to more learneroriented styles of teaching. However, the rewards of adopting SRL to guide, frame, and motivate music learning have now become obvious. And for us, ingredients that enable optimized learning, including an intellectual curiosity and emotional engagement with the music being learned, blossom most effectively when learners are able to take control of their learning in the ways described in this and other chapters within this volume. Acknowledgements Funding note. This research was funded by an Australian Research Council Discovery Grant DP150103330 held by the first two authors. Note 1 The first-person ‘we’ and ‘our’ throughout the chapter refer to any or all authors of the chapter and our colleagues as indicated in citations. References Bennett, D. (2008). Understanding the classical music profession: The past, the present, and strategies for the future. Aldershot, UK: Ashgate. Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78, 246–263. Bonneville-Roussy, A., & Bouffard, T. (2014). When quantity is not enough: Disentangling the roles of practice time, self-regulation and deliberate practice in musical achievement. Psychology of Music, 43, 686–704. Calderhead, J. (1981). Stimulated recall: A method for research on teaching. British Journal of Educational Psychology, 51, 211–217. Cleary, T. J., & Callan, G. L. (2018/this volume). Assessing self-regulated learning using microanalytic methods. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed.). New York: Routledge. Cleary, T. J., Callan, G. L., & Zimmerman, B. J. (2012). Assessing self-regulation as a cyclical, context-specific phenomenon: Overview and analysis of SRL microanalytic protocols. Education Research International, 2012, 1–19. Creech, A., & Gaunt, H. (2013). The changing face of individual instrumental tuition: Value, purpose, and potential. In G. E. McPherson & G. F. Welch (Eds.), The Oxford handbook of music education (pp. 694–711). Oxford, UK: Oxford University Press. Davidson, J., Howe, M., Moore, D., & Sloboda, J. (1996). The role of parental influences in the development of musical ability. British Journal of Developmental Psychology, 14, 399–412.
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13 Self-Regulation in Athletes A Social Cognitive Perspective Anastasia Kitsantas, Maria Kavussanu, Deborah B. Corbatto, and Pepijn K. C. van de Pol Introduction Acquiring expertise in sport requires high levels of self-regulation and self-motivation (Kitsantas & Kavussanu, 2011). Elite athletes and coaches often mention self-regulation as one of the most important factors for their success. For example, top college football coach Lou Holtz said “Without self-discipline, success is impossible, period.”1 Similarly, researchers and practitioners have focused their attention on gaining a better understanding of athletes’ self-regulatory functioning in sport, exercise, and physical education contexts (Gaudreau, 2014). In this chapter, we describe key processes of athletes’ self-regulation from a social cognitive self-regulatory perspective (Usher & Schunk, 2018/this volume; Zimmerman, 1989). We use the term ‘self-regulation in sport’ to refer to self-initiated thoughts, feelings, and actions that athletes use to attain various goals (Zimmerman & Kitsantas, 2005). First, we review a social cognitive cyclical model of self-regulation that describes key selfregulatory processes and motivational beliefs. Second, we discuss empirical studies that have examined athletes’ key self-regulatory processes (e.g., goal-setting, goal orientation, self-monitoring, and self-evaluating) and coach influences such as motivational climate on athletes’ self-regulatory functioning. Third, we discuss implications for practice regarding how to enhance learning and performance in sport using social learning experiences. Finally, we provide directions for future research. A Social Cognitive Perspective of Self-Regulation in Athletes Zimmerman (2000) developed a social cognitive model of self-regulation that includes motivational beliefs and cognitive processes in three cyclically interrelated phases: forethought, performance, and self-reflection. The forethought phase precedes athletes’ engagement in a task and includes task analysis processes (e.g., goal setting and strategic planning) and motivational beliefs (e.g., self-efficacy, task value, goal orientation) that facilitate athletes’ preparation and motivation to engage in self-regulated learning. Elite athletes and coaches often comment on the importance of these forethought processes. For example, former top tennis player Chris Evert stated “You’ve got to take the initiative and play your game. In a decisive set, confidence is the difference,” whereas football coach Homer Rice commented on the importance of self-motivation in learning and performance: “You can motivate by fear, and you can motivate by reward. But both those methods are only temporary. The only lasting thing is self-motivation.” In the performance phase, athletes are actively involved in learning a task, observing their performance, and using strategies to facilitate the attainment of their goals. During this phase, an athlete uses strategies such as imagery, help seeking, self-instruction, and self-observation techniques such as self-recording. “When you record your training, it crosses the line between being casual or serious about the sport,” says Roisin McGettigan, an Irish track-and-field athlete. She also stated: “I was able to track my progress, learn what worked and what didn’t. I could figure out why I was tired and see if I over- or under-estimated my training.” It is important to note that the effectiveness of these strategies depends on how well an athlete engages in self-observation. The self-reflection phase involves processes that follow learning and/or performance efforts, for example selfjudgment and self-reactions. Self-judgment refers to self-evaluation and attributions, while self-reactions are selfsatisfaction/affect or adaptive inferences (e.g., athletes engage in goal adjustment based on outcomes) and defensive inferences (e.g., athletes engage in procrastination or task avoidance). It is in this phase that athletes judge their performance and assign causes to their outcomes based on prior self-monitored data. For instance, a baseball pitcher, who is struggling with his command (e.g., consistently missing the strike zone) may reflect on his performance and attribute his failure to the mechanics of his delivery (e.g., the wind up, the leg kick) instead
of internalizing the failures in terms of being ‘a bad pitcher’. Self-reflection phase processes, in turn, influence forethought phase processes. For example, if a volleyball player keeps failing to score on a serve, she may begin to doubt her serving capability and may feel the need to review her technique and engage in more practice episodes. This phase completes the self-regulatory cycle of learning and influences subsequent efforts to learn and perform (e.g., goal adjustment, strategy selection, etc.). Self-regulated learning in sport can be summed up with a quote from Brittany Bowe, an American Olympic speed skater, who stated: “You have to be a student of the game to be successful, and it’s promising when you can say that with a world-record performance, I still have things to improve on!” Indeed, expert athletes create selforiented feedback loops to monitor the effectiveness of strategies and to adapt their functioning to maintain and improve performance outcomes. Research on Self-Regulated Learning and Performance in Sport There is extensive research evidence that athletes use a broad array of self-regulatory processes (Kitsantas & Kavussanu, 2011; Kitsantas & Zimmerman, 2002). In this section, we review pertinent research on the effectiveness of key self-regulatory processes across the three cyclical phases of self-regulated learning and performance, described in the previous section. Forethought Phase Goal Setting Goal setting refers to identifying intended actions or outcomes. A number of studies have demonstrated that setting specific rather than general goals, short-term rather than long-term goals, and self-generated rather than assigned goals is more beneficial for learning and performance in sport (e.g., Kolovelonis, Goudas, Dermitzaki, & Kitsantas, 2013; Kitsantas & Zimmerman, 1998). In addition, moderately difficult rather than easy goals are the most effective goals in increasing achievement and motivation (Locke & Latham, 1990). Finally, process goals, which require athletes to focus on the processes (steps) rather than the end result promote athlete attention to detail and produce better performance compared to outcome goals, which focus purely on the end result (Zimmerman & Kitsantas, 1996, 1997). Premature focus on outcomes before a skill is fully mastered increases the cognitive complexity of a skill and has a negative impact on motivation (Zimmerman & Kitsantas, 1996, 1997). Effective learning and performance best occur when one initially concentrates on process goals, then switches to an outcome goal when the task becomes automatic. For example, in an experimental study, where students were assigned to three experimental conditions (outcome goal, process goal, and a shifting process-outcome goal condition), participants who shifted from process to outcome goals outperformed students who maintained their process goal after reaching automaticity. These findings suggest that athletes should use both process and outcome goals depending on their phase of learning (Kitsantas & Zimmerman, 1998; Zimmerman & Kitsantas, 1997). Goal Orientation The best motivation always comes from within. —Michael Johnson (Gold Medal Sprinter) Achievement goal orientation, a central construct in achievement goal theory (Ames, 1992; Dweck, 1996; Nicholls, 1989), has received a lot of research attention in relation to self-regulation in the athletic domain. Two major achievement goals operate in sport and involve variation in the definition of competence and the criteria one uses to evaluate success. The terms task, learning, and mastery have been used by Nicholls (1989), Dweck (1996), and Ames (1992), respectively, to refer to a goal in which an individual strives to develop competence
and evaluates success using self-referenced criteria (e.g., learning, task mastery). The terms ego, performance, and ability have been used by these theorists to refer to a goal, where one strives to demonstrate competence and evaluates success using other-referenced criteria (e.g., winning). Several studies have examined goal orientation in relation to self-regulation processes in physical activity contexts. Theodosiou and Papaioannou (2006) found that task orientation was a positive predictor of selfmonitoring and evaluation, whereas ego orientation was unrelated to self-monitoring, in physical education students. Elite young athletes characterized by a high task/high ego profile used imagery more often than those characterized by a low task/moderate ego and moderate task/low ego profile (Harwood, Cumming, & Hall, 2003); in a second sample of elite athletes, those in the high task/moderate ego group reported more frequent use of goal setting, self-talk, and imagery compared to the low task/high ego and moderate task/low ego groups (Har-wood, Cumming, & Fletcher, 2004). Finally, in an experiment involving undergraduate student athletes (Gano-Overway, 2008), participants in the task-involving condition (i.e., focus on improving their reaction time), used collectively more self-regulation strategies (i.e., planning, monitoring, goal setting, task strategies, and self-evaluation) while performing a novel computer reaction-time task, compared to those in the ego-involving condition (i.e., focus on outperforming others). These results suggest that task orientation is the critical goal in influencing the use of selfregulatory processes in athletes, but a high ego goal may have complementary benefits. More recently, researchers have examined whether achievement goals differ as a function of the context, namely training and competition. Organized training (i.e., training sessions under supervision of a coach) is central to athletes’ sport lives, as this is the context where they spend a vast amount of time to develop their skills, particularly at the elite level. Organized competition is an integral part of sport, which by nature involves social comparison. Several studies have shown that ego orientation is higher in competition than in training, whereas task orientation is more stable across the two contexts (van de Pol & Kavussanu, 2011, 2012; van de Pol, Kavussanu, & Ring, 2012a). This line of research has also investigated the effect of context on the relationship between goal orientations and self-regulation strategies. In the first study to examine this issue, van de Pol and Kavussanu (2011) found that task orientation was positively related to goal setting and self-talk in both training and competition and attentional control in competition; ego orientation was unrelated to these strategies in either context. Task orientation was also positively associated with effort and perceived improvement in training as well as with perceived performance in competition. Finally, an interaction effect emerged, whereby ego orientation was linked to effort in competition, when athletes had low or average levels of task orientation. These findings suggest that athletes, who tend to evaluate success using self-referenced criteria, are more likely to use self-regulation strategies not only when they practice but also when they compete. These athletes are also more likely to try hard in both contexts. However, when athletes’ task orientation is low or average, ego orientation may be beneficial for effort in the competition context. In a recent experiment, van de Pol, Kavussanu, and Ring (2012b) created training and competition conditions in the laboratory. Participants—tested in pairs—were told that the purpose of the (training) session was to learn and improve the skill of golf putting, were given instructions on how to put a golf ball, practiced six blocks of ten putts each, and then they performed ten putts in a zero-sum competition condition. ‘Zero-sum competition’ involves a competition in which athletes compete against each other on a ‘winner-takes-all’ basis, i.e., one person/team either wins or loses (Stanne, Johnson, & Johnson, 1999). Ego involvement increased and task involvement decreased as participants moved from training to competition. Although ego involvement positively predicted effort and enjoyment in both conditions, the effect of ego involvement on these variables was augmented when task involvement was also high. Participants who scored higher in ego involvement performed better in competition. Thus, once a task has been mastered a motivational focus on an ego goal in competition may benefit performance. Another line of research investigating the role of achievement goals on emotions has shed more light on the role of task and ego goals on achievement-related outcomes. In one study (Dewar & Kavussanu, 2011), golfers who
reported being task involved were more likely to also experience happiness and excitement, and less likely to feel dejection during a round of golf. In addition, when they perceived that they had performed poorly, the greater their ego involvement, the less happy and more dejected they were. Thus, ego involvement could lead to negative emotions when one performs poorly. In a second study of team-sport athletes (Dewar & Kavussanu, 2012), ego involvement positively predicted hope when perceived performance was high and dejection when perceived performance was low. Task involvement was positively related to happiness, pride, and hope, and negatively related to dejection and shame. These results suggest that task involvement is a robust predictor of positive emotions, whereas the relationship between ego involvement and emotions depends on how athletes perceive they have performed. The findings discussed in this section underline the significance of adopting a task goal when one learns and performs motor skills, as it appears to be the most adaptive goal facilitating a variety of self-regulation strategies and achievement-related outcomes. The overall findings pertaining to an ego achievement goal are somewhat more complex. A combination of high task and high ego seems to be the most beneficial goal profile to adopt in competition, particularly concerning effort and performance. However, ego orientation may predispose athletes to experience negative emotions when they do not perceive they performed well, which may influence their selfregulatory functioning. Self-Efficacy Self-efficacy refers to “beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments” (Bandura, 1997, p. 3). There is extensive evidence that athletes’ self-efficacy influences their motivation to initiate and sustain self-regulatory functioning. This construct influences many selfregulatory processes such as goals, task strategies, and self-reactions. For example, athletes reporting high selfefficacy are more likely to set challenging goals and search for strategies that will help them accomplish these goals compared to those with low efficacy (Kitsantas & Zimmerman, 2002, 2006). Self-efficacy has also been linked to sport performance, with a moderate relationship reported in a meta-analysis of 45 studies (Moritz, Feltz, Fahrbach, & Mack, 2000). More recently, Halper and Vancouver (2016) examined the effects of Division I college football players’ self-efficacy beliefs on their squat performance, measured at three points in time. Multi-level analysis showed that athletes’ self-efficacy was positively related to squat performance, both at between-person and within-person levels of analysis (while controlling for past performance). These findings provide evidence that athletes’ self-efficacy is positively associated with their performance in sport. Performance Phase Self-Monitoring A key process in the performance phase of self-regulation is self-monitoring, which refers to observing and tracking one’s own performance. Self-recording, a form of self-monitoring, in sport can greatly assist selfmonitoring because it can increase the proximity and accuracy of feedback (Zimmerman & Kitsantas, 1996, 1997). In a study of students initially learning to throw darts, participants who engaged in self-monitoring (e.g., kept records of their performance) were more successful in learning to throw darts and reported greater motivation than the groups who did not self-monitor (Zimmerman & Kitsantas, 1996). There is evidence that highly self-regulated learners use self-recording more often than poorly self-regulated athletes. Kolovelonis, Goudas, and Dermitzaki (2011) examined the effects of self-recording, along with goal types (process, performance, outcome, and multiple goals) in 105 fifth and sixth graders using a dart-throwing task. Self-recording had a positive effect on students’ performance of a new skill. Using self-recording during practice allowed them to compare their own performance with their goals; however, self-recording had no effect on their satisfaction with learning the new skill. Overall, these findings suggest that when teaching a sport skill, self-recording should be used as an aid to improve learning.
Adaptive Help Seeking Adaptive help seeking involves approaching experts, such as coaches, to request assistance with one’s learning. It involves seeking specific assistance to correct flaws in one’s performance and is limited in duration. Help seeking is an important part of the self-regulation process because the learner needs to determine whether help is necessary, decide whether to seek help, the type of help and from whom, and solicit, obtain, and process whether the help received will enable them to achieve their goal (Karabenick & Dembo, 2011). A supportive social environment may allow leaners to seek help, knowing that a strategy can lead to success. Research related to athletes and help seeking is limited. However, evidence suggests (Durand-Bush & Salmela, 2002) that Olympic and world championship athletes report that help is critical and they seek help not only from their primary sport coach, but from other professionals such as strength and conditioning coaches, sport psychologists, athletic trainers, and nutritionists. Thus, help seeking is an important self-regulatory process and may have implications for athletic performance. Self-Reflection Phase Self-Evaluation Self-evaluation refers to using standards to make self-judgments about one’s performance. Athletes evaluate their performance based on standards from previous performances (e.g., comparing current with previous performance), mastery goals (use a sequence of steps that leads to the outcome), and performance of other competitors. Kitsantas and Zimmerman (2006) examined whether graphing performance in combination with self-evaluation influenced dart-throwing performance. They found that novice learners, who graphed their performance and evaluated their outcomes based on graduated or hierarchically set standards significantly improved their dart-throwing skills and experienced more positive motivational beliefs than students who did not graph and evaluate their outcomes on graduated standards. Closely linked to self-evaluation are the attributions athletes make for their errors. Attributing errors to strategy deficiency sustains motivation when performance outcomes do not meet desired standards (Zimmerman & Kitsantas, 1997, 1999). Self-evaluative and attributional judgments lead to different types of self-reactions. Two forms of self-reactions have been explored: satisfaction and adaptive inferences, which direct athletes to sustain or change their self-regulatory processes, and defensive inferences, which may lead to apathy, procrastination, and task avoidance. Highly self-regulated athletes make adaptive inferences, such as re-examining the effectiveness of their strategies, whereas poorly self-regulated individuals report defensive inferences, which serve primarily to protect them from future dissatisfaction (Zimmerman, 2000; Kitsantas & Zimmerman, 1998). Research on Coaching Influences on Self-Regulated Learning and Performance Training in Key Self-Regulatory Processes Coaches can play a key role in supporting and promoting athletes’ self-regulatory functioning. From a cyclical perspective of self-regulation (Kitsantas & Zimmerman, 2002; Zimmerman & Kitsantas, 1996, 1997), athletes acquire skills in four sequential levels: observation, emulation, self-control, and self-regulation (Zimmerman & Kitsantas, 2005). At the observational level, the coach models and describes a specific strategy that will assist the athlete to improve athletic skills. Then, the coach asks the athlete to emulate the model’s strategy and provides him or her with corrective feedback and praise. Once the strategy related to a specific sport skill is mastered , at the self-control level, the coach slowly withdraws support and asks the athlete to practice on his or her own in a controlled setting using the strategy and process-oriented standards. Finally, at the self-regulated level of skill acquisition, the athlete is asked to focus on outcomes and adjust his or her performance as needed. At this level, an athlete’s functioning continues to depend on the coach. This multi-level model of self-regulation has been tested in many studies (e.g., Kitsantas, Zimmerman, & Cleary, 2000; Zimmerman & Kitsantas 1996, 1997). In a recent study with fifth and sixth grade students, Kolovelonis
Goudas, Dermitzaki, and Kitsantas (2013) used this model as a method for teaching basketball dribbling. Findings showed that students improved their basketball dribbling when they progressed through the levels of the model providing support for the use of the four-level model in developing novice learners’ sport skills. However, we do not know whether the findings can be replicated in elite athletes using a longer intervention. Research comparing expert, novice, and non-expert athletes also provides evidence for how coaches can structure the learning environment to foster self-regulation. Cleary and Zimmerman (2001) found that expert basketball players displayed higher levels of self-efficacy, set more specific goals, used more process-oriented strategies, and attributed failure to strategy more so than did non-expert and novice athletes. Similar findings were revealed in a study of volleyball players (Kitsantas & Zimmerman, 2002). It may be that experts display higher levels of performance because they engage in more effective self-regulatory processes than non-experts and novices. Similarly, Walsh (2012) stressed that from a purely operational perspective, as athletes’ skill level improves, the feedback and instruction should be pared down, allowing them to master the exercise using more self-regulatory strategies. Researchers have also investigated coaches’ abilities to assist their athletes in their self-regulatory processes. Collins and Durand-Bush (2014) implemented an intervention based on Zimmerman’s social cognitive model of self-regulation (Zimmerman, 2000) with one coach and one elite international curling team. The coach delivered eight 90-minute intervention sessions to his team along the themes of awareness, control, and adaptation of behaviors and goals. Results indicated that the coach used multiple strategies to assist the athlete with preparation for competition (forethought phase), such as intrinsic/autonomous goal setting and strategic planning. During the performance phase, coaches had much less influence on the athletes’ self-regulatory skills, but they could help the athletes track their progress and give feedback and attention-focusing encouragement. In the self-reflection phase, the coach helped the athletes digest their statistical data, suggested corrective strategies, and guided them in finding solutions for issues that arose during performance. This study provides support for the role a coach can play in assisting their athletes using self-regulatory strategies. Smith, Ntoumanis, and Duda (2010) asked regularly training athletes at the beginning of their season to select a personal sport goal to work toward and to indicate whether the goal was self-generated, whether they had a strategy to achieve it, the extent to which their coach supported their goals (i.e., was autonomy supportive versus controlling). Participants were retested at the mid-point and the end of their season. Athletes’ perceptions of their coaches’ support for their autonomy and participation in training decisions were positively associated with autonomous goal motives, whereas athletes who perceived their coaches as controlling also adopted controlled goal motives. The findings of this study suggest that coaches should avoid the use of controlling behavior such as reprimanding athletes for performing below the coach’s expectations, as this could negatively affect the athletes’ motivation. By nurturing the motivation of an athlete (e.g., by allowing some choice about the training, giving rationale for practice tasks, and giving clear, non-critical feedback) the coach can provide an environment that may have a favorable influence on athletes’ motivation and performance. Researchers also argue that what matters the most is the ability to strategically retune a goal (e.g., Smith & Ntoumanis, 2014). Smith and Ntoumanis (2014) examined the role of goal motives (i.e., autonomous or personal internal goals vs. controlled or external goals) in predicting self-regulatory responses to unattainable goals in athletes. Goal motives could forecast how easy it is for the athlete to disengage from the sport goal and reengage in an alternative goal. This is important for an athlete when a goal becomes unattainable, as failure can be psychologically damaging to the athlete and their performance if they are not able to disengage from this unattainable goal and reset a more realistic goal. Overall, the findings suggest that it is critically important to examine athletes’ motives and behaviors when they have trouble meeting a goal. That is, coaches might facilitate awareness of the unachievable goal and help the athlete develop a strategy for disengaging from the goal and shifting to an alternative attainable goal.
Motivational Climate Motor learning and performance typically occur in group settings. In these settings, the coach or physical education teacher plays a vital role in influencing participants’ self-regulation and motivation. The main aspect of the coaching environment that has been examined in relation to self-regulatory processes and motivational beliefs is the motivational climate of the team. Motivational climate is a term coined by Ames (1992) to refer to the achievement goals that are salient in the achievement context and is created by significant others such as physical education teachers, coaches, and parents. These individuals structure the achievement context in a manner that conveys to participants the criteria for success through the evaluation procedures, the distribution of rewards, the type of feedback they provide, and other means. A mastery climate is evident when success is defined as individual progress, every person has an important role, and the focus is on skill improvement and development. A performance motivational climate is salient when success is defined in normative terms, the top athletes typically receive the most recognition from the coach, and the emphasis is on how one’s ability compares to that of others (Ames, 1992). Several studies have shown a clear link between mastery climate and the use of self-regulatory strategies and intrinsic interest in physical education. Theodosiou and Papaioannou (2006) found that mastery climate was positively related to self-monitoring and evaluation, while performance climate was unrelated to self-monitoring and was weakly related to evaluation. Ommundsen (2006) showed that mastery climate positively predicted metacognitive self-regulation, regulation of effort, and adaptive help seeking. The effects of mastery climate on help seeking were mediated fully by task orientation, while the effects on meta-cognitive strategies and effort regulation were partially mediated by this variable; this suggests that mastery climate may have positive effects on self-regulation in part because it enhances task orientation. Performance climate was a positive but weak predictor of meta-cognitive regulation. Finally, physical education students who perceived a mastery climate in their class were more likely to concentrate better in their physical education lesson (Papaioannou & Kouli, 1999). Motivational climate has also been associated with adaptive motivational beliefs and affective reactions. Undergraduate university students enrolled in tennis classes who perceived a high mastery climate in their class reported significantly more intrinsic interest in the activity and exerted more effort compared to students who perceived a low mastery climate in their class (Kavussanu & Roberts, 1996). Interestingly, female—but not male—students who perceived a mastery climate in their class were more likely to report high self-efficacy. These females also had low perceptions of ability. Perhaps a mastery motivational climate is most beneficial for those individuals (i.e., females) who tend to doubt their physical abilities (Kavussanu & Roberts, 1996). Motivational climate has also been investigated separately in the contexts of training and competition. Van de Pol, Kavussanu, and Ring (2012a) asked football players to complete questionnaires measuring perceived motivational climate, effort, enjoyment, and tension in these two contexts. Participants who perceived a mastery motivational climate in their team reported more effort in training, and more enjoyment in training compared to competition. It is worth noting that both effort and enjoyment were higher in competition than in training, which may explain these findings. That is, a mastery climate appears to be more beneficial during training, where athletes’ enjoyment and effort are not as pronounced as they are in competition, making it even more important that coaches create a mastery climate in training. Performance climate was negatively associated with effort, and positively associated with tension, in both contexts. A robust finding of the studies reviewed above is the positive link between mastery climate and self-regulation processes as well as motivational beliefs. This finding suggests that creating a teaching or coaching environment that focuses on individual learning and personal success could lead learners to set more effective goals, monitor and evaluate their performance, but also regulate their effort, seek help, and seek feedback to improve their skills. This climate may also lead to more satisfaction with performance and enjoyment of the activity and greater concentration. In contrast, performance climate does not have consistent effects on self-regulation.
Future Research Directions In this chapter, we reviewed research emphasizing the importance of engaging athletes in self-regulated learning. We also discussed how self-regulatory functioning can vary from training to competition and the role of the coach in this process. In this section, we offer some directions for future research. First, more research should be conducted on self-regulation in training and competition contexts. Goals represent an important aspect of self-regulation as they provide a clear picture of situation-specific strategies that individuals plan to use as well as the outcomes they seek to attain. Although recent research suggests that athletes may functionally adjust their goals in the context of training and competition (van de Pol, Kavussanu, & Ring, 2012b), it may be valuable to examine this issue from a self-regulation perspective, and in particular, how contextual motivational processes can be integrated into the cyclical model as proposed by Zimmerman (2000). Second, the integration of technology is exploding in sport, particularly in the area of self-monitoring. Currently, athletes can monitor their physiological responses such as heart rate or maximum oxygen uptake through wearable apparel. They can measure their mental processes through applied biofeedback such as brain wave (quantitative EEG) measurement. And they can work on their motivation through apps on their phone. The integration of technology into the self-regulated learning platform is an area for future research. Third, very few research studies have focused on coaches’ self-regulatory functioning. Coaches can also employ self-regulation, and as they become aware of the impact self-regulated learning can have on performance, they can begin to apply this knowledge to their own coaching techniques and reflective processes. More research is needed in this area as the use of self-regulated learning processes in coaches may affect athletic performance. Finally, researchers could conduct qualitative studies to better understand self-regulatory processes that are specific to the context of sport, and based on these findings develop instruments to assess self-regulatory functioning in athletes. Nurturing Athletes’ Self-Regulation in Training and Competition: Implications for Practice Rafael Nadal, after his first-round loss against Fernando Verdasco at the Australian Open 2016, stated: “Today I was not ready to compete the way that I was practicing, so not happy with that. That’s it.” Athletes continuously move between training and competition, and they may need to adapt their achievement goals to adapt to these contexts. A self-regulated athlete functionally adjusts his/her goals to these contextual affordances to obtain desirable outcomes such as skill improvement in training and performance in competition. Below, we present a practical example of how self-regulatory processes function when an elite tennis player moves from training to competition. In training, the player may engage in the forethought phase; in this context, skill improvement is an objective typically desired and emphasized; for example coaches are likely to create a mastery motivational climate in training (van de Pol, Kavussanu, & Ring, 2012a). A self-regulated tennis player should be able to pick up these contextual cues (see Zimmerman, 2002) and may adopt a high task goal based on the self-motivational beliefs and knowledge so that this goal may assist him in obtaining desirable outcomes. Adopting this goal may facilitate a feeling of competence when players improve, and this should result in sustained motivation, which is essential to cope with the large number of training hours required for realizing one’s full potential. These forethought processes should influence how the player engages in the performance phase, when the training actually starts. Assuming that the tennis player strives for skill improvement in training, the drills and exercises are performed by using self-monitoring and control strategies (e.g., self-talk and attentional focus), which should facilitate this objective. Once the training is completed, the player may engage in self-reflection, thus evaluate his or her performance and assign causal attributions to the outcomes. For example, if the player has endorsed predominantly a task goal in the forethought phase, he or she may positively evaluate skill improvement by
attributing improvement to effort. This may lead to positive affect such as enjoyment and satisfaction in training (van de Pol & Kavussanu, 2011, 2012; van de Pol, Kavussanu, & Ring, 2012a). When an important match is coming up the tennis player needs to shift his or her motivational focus to a ‘competition mind state’. In this context winning is important, as the competitive outcome determines his ranking and potential prize money. Thus, in the forethought phase, the player may adopt a more ‘balanced goal profile’: a task goal, which may help him/her to reach an optimal personal performance standard, combined with an ego goal, which may provide him/her with just that extra effort to persist when faced with challenges during a tough match (see van de Pol, Kavussanu, & Ring, 2012b). When the match starts, the athlete engages in the performance phase where the task will be executed, and he/she may adjust his/her monitoring and control strategies to the desired goal(s). In this context, the player may focus less on technical (body movements) and more on tactical skills (ball placement), as this may benefit performance. Finally, the match is finished and the athlete engages in the self-reflection phase where she or he evaluates his or her own performance and assigns causal attributions to it based on the adopted (high task/high ego) goal profiles in this match. The player evaluates whether he or she reached optimal personal performance standards (task goal) but also reflects on the outcome (win or loss) of the match (ego goal). For example, if the match was lost the player would attribute this loss to a controllable but unstable factor (e.g., ‘I lost because I didn’t put enough effort in today’s match’) which may help to protect their self-efficacy beliefs for the next match. In preparation of the next competition the athlete will use the knowledge from the last match and make adjustments needed for the next training episode. When an athlete is less adept at self-regulated learning, it becomes important for the coach/teacher to intervene. The ability of the coach to create an appropriate performance environment, stressing appropriate forethought, performance, and self-reflective processes, is critical. When athletes (learn to) effectively use these selfmotivational beliefs and strategies across the two contexts this may contribute to a fulfillment of the innate need for mastery and satisfaction from competing against other athletes, but it also helps them to become more selfregulated learners and performers. In the end, this may lead to higher achievement standards in both training and competition but also to a more enjoyable and enduring sport participation. Kimiko-Date Krumm comments after becoming, at age of 42, the oldest winner of a women’s-singles match in Australian Open History, “I love tennis. I like practice. I like games. I like the tour. I enjoy it a lot” (Australian Open, 2013). Coaches can also play an important role in nurturing athletes’ self-regulation and maintaining their motivation. Coaches need to be aware of the importance of creating a mastery motivational climate, particularly in training, where general levels of effort and enjoyment are lower compared to competition (van de Pol & Kavussanu, 2012; van de Pol, Kavussanu, & Ring, 2012a, 2012b). By focusing on each individual athlete, emphasizing skill learning and development, and rewarding effort, coaches and physical education teachers can create an environment that is likely to maximize effort, enjoyment, and the use of self-regulation strategies in physical activity contexts. Coaches also need to de-emphasize a performance motivational climate, particularly in training, as it has been associated with higher tension in athletes in this context (van de Pol, Kavussanu, & Ring, 2012a). Finally, in assisting athletes in setting autonomous goals and re-engaging in realistic goals when the prospect of achieving their goals are weak, coaches can help athletes perform at the highest levels. Conclusion In conclusion, in this chapter we reviewed research on self-regulation in sport and physical education, and examined the role of coaches and teachers in fostering athletes’ self-regulatory functioning. Research findings in physical education and sport settings show that self-regulation is consistently associated with high levels of performance. Creating a mastery motivational climate that encourages skill development and supports athletes’ independence is likely to enhance their self-regulation, motivation, and ultimately performance.
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14 Self-Regulation An Integral Part of Standards-Based Education Marie C. White and Maria K. DiBenedetto Standards have been developed across multiple disciplines and are established to ensure students are learning content critical for the discipline and that assessments are aligned with the content (Pennington, Obenchain, Papola, & Kmitta, 2012). Standards are the basis for all other educational constructs and laws associated with implementation of curriculum development, instruction, and assessment of student learning. In the field of education, standardsinvolve a cumulative body of knowledge and set of competencies that are the basis for quality education. A standards-based education refers to a shared set of expectations of what students should learn and how they will demonstrate learning through assessments (Kendall, 2011). Its impact on education is signifi-cant because it guides curriculum, instructional design, and assessment instruments while providing opportunities for consistency of learning outcomes within and across international learning communities (Marzano, Yanoski, Hoegh, & Simms, 2013). In the current state of educational reforms, standards have gained more attention as a policy rather than the criteria that make evaluations possible. Internationally, evaluations and assessments of students, teachers, schools, and education systems are based on a set of standards-based performances as defined by the government. Each country individually defines the knowledge and skills students are expected to have attained at different stages of their education. Implementers of the policy develop curriculum that covers the objectives identified in the standards and the assessments that indicate attainment of those standards. The results are only valid when the key elements of curriculum, assessment, and standards are well matched (OECD, 2013). During the last few years, researchers have uncovered an association between self-regulated learning, standards, and standards-based education. Self-regulated learning (SRL) refers to the cyclical processes through which learners organize and direct their behavior, actions, and thoughts in order to attain specific goals (Zimmerman, 2000). In each phase of SRL, standards are the criteria for generating evaluations and monitoring goal progress (Winne & Hadwin, 2008; Zimmerman, 1998). Although research in self-regulation has focused on standards and standards-based education, much less is known about the actual nature of these associations. This chapter examines the role of self-regulation as an integral part of standards-based education in theory and application. Section one addresses the theoretical grounds linking SRL to standards-based education. Section two presents research evidence aligning SRL to standards in the classroom context. The importance of feedback, formative assessment, human agency, self-efficacy, motivation, and engagement in a standards-driven learning environment is examined in this section. The third section suggests future research directions in several areas that have not yet been investigated linking SRL to standards-based education. The fourth section describes implications for educational practice that could view standards through an SRL lens and provide teachers with a framework to translate standards into achievable goals. Theoretical Foundations Linking Standards to Self-Regulated Learning Often, the terms standards and goals are used interchangeably; however, they are not the same. Standards are generalized to describe the level at which a learner’s accomplishments are considered to be competent in a particular area of study. They can be used to describe and communicate to the learner what counts as quality learning or as good practice, and they guide teacher’s choices in lesson planning. In addition, standards can be used as measures or benchmarks and as tools for decision-making, indicating the distance between actual performance and the minimum level of performance required to be considered competent. Goals, in turn, are derived from standards and focus the learner on both the process and the outcome. In self-regulation, goals are specific, short-term, and set within a level of difficulty that is challenging but does not lower the learner’s self-
efficacy (Schunk, 1989). Teachers and students use standards as a gauge to self-monitor and evaluate goal progress towards a specific target. Learning standards are like targets, blueprints, or roadmaps for student learning and performance. They set the destination for what students need to know and be able to do, as well as the benchmarks they should reach along the way (Carmichael, Martino, Porter-Magee, & Wilson, 2010). Teaching and learning standards that include goals and objectives for self-regulation have been a guiding force behind successful learning outcomes and have contributed to the understanding of the application of social cognitive theory to learning and performance (Schunk, 1989). In this context, self-regulation is conceptualized as a cyclical process with three phases: forethought, performance, and self-reflection. The phases are cyclical because each process within each phase of self-regulation influences the next (see Usher & Schunk, 2018/this volume; Zimmerman, 2000). Learning objectives that engage students in problem solving and critical thinking can be derived from a standardsbased curriculum using SRL strategies to guide the learner to set process goals (Zimmerman & Kitsantas, 1997). Active self-regulated learners engage in processes that are critical for setting the learning stage in the fore-thought phase such as rating self-efficacy and goal setting. This phase involves the feelings, thoughts, and behaviors of students as they prepare to learn something. During the performance phase, students are trying out different strategies and monitoring their progress as they work on the learning task. Essential processes for guiding and controlling behavior in this phase include metacognitive monitoring and attention focusing. The self-reflection phase is pivotal for guiding future cycles as students evaluate and attribute feedback on performance to effort and strategy use after the work is completed (see Usher & Schunk, 2018/this volume). Self-regulation is dynamic because learners continually evaluate and adapt their performance against a standard or a modeled behavior through three cyclical phases (Schunk & Zimmerman, 1998). Self-regulation also requires a level of competency before learners can begin to use self-regulatory strategies outside of the presence of a model or teacher. Standards can serve as a guide to promote the development of self-regulation, through the strategic use of self-monitoring, formative assessment, self-evaluation, feedback, goal setting, learning objectives, and selfreflection (Bembenutty, White, & Vélez, 2015). Developing self-regulatory competency begins at the observation level. Teachers model a standard of performance, which they convey to students as they demonstrate feelings of competency and the strategies for task success. Teachers model how to evaluate their self-efficacy beliefs for a specific task and how to choose strategies for self-set goals that are in line with outcome expectations for standards-based performance. This type of modeling provides behavioral indicators for students at all grade levels on how to engage in and how to practice self-regulation (White & DiBenedetto, 2015). The emulation level affords teachers the opportunity to reinforce outcome expectations while they provide learners with continuous feedback and modeling. As students begin to experience a sense of mastery, the teacher remains engaged with learners making sure their efforts increase rather than diminish self-efficacy. For instance, closely monitoring students as they practice an algebraic problem can help prevent students from feeling frustrated if unable to solve the problem correctly. As learners begin to experience self-efficacy for the given task, they transition into the third level of self-control. The learner has now retained a mental image of the standard and feels capable of making progress towards achieving the outcome expectations set at the beginning of the task. The teacher’s role shifts at this level. Here the student engages in the task with minimal support from the teacher. The teacher provides assistance when requested by the student who is beginning to take more control of the pacing of the learning. When self-regulation is achieved, at the fourth level, the learner is able to systematically adapt performance to novel situations. The learner has high levels of self-efficacy and is motivated to set challenging goals in line with even higher learning outcomes (Zimmerman, Schunk, & DiBenedetto, 2015). Some students will need to remain at observation, emulation, and self-control levels longer than others and reset goals based on feedback and self-evaluation. With this in mind, White and DiBenedetto (2015) proposed an integrated model of SRL that requires teachers to identify indicators of both levels and phases of self-regulation for individual students when engaged in specific learning tasks. The integrated model situates phases (i.e.,
forethought, performance, and self-reflection) into levels of self-regulation (i.e., observation, emulation, selfcontrol, and self-regulation) providing a framework for SRL in the twenty-first-century classroom that includes increased cognitive modeling (Bembenutty, Cleary, & Kitsantas, 2013; Pape, Bell, & Yetkin-Özdemir, 2013). In this model, teachers enhance the learning environment by carefully pacing students according to their status at each level while guiding them individually through the three phases of SRL, providing feedback and criteriabased formative assessment (see Figure 14.1 with forethought indicated by 1, performance by 2, and selfreflection by 3). In the integrated model of SRL, at level one students observe the teacher model the forethought, performance, and self-reflection phases of the SRL cycle. During the forethought phase, students observe how to set proximal goals aligned with standards-based performance for a specific task. Criteria for goal attainment are examined and explained as the teacher engages the students in planning strategies and self-efficacy assessments. At the conclusion of the forethought phase the teacher shifts to the second phase, performance, modeling an actual enactment of the task. Students are reminded to pay close attention to how SRL strategies assist in attaining the goals set during the forethought phase. As the teacher demonstrates self-monitoring, progress is determined by checking performance against the criteria established for successful learning outcomes. In the final phase of the cycle, self-reflection, the teacher models self-evaluation by assessing the completed task with the criteria for goal attainment.
Figure 14.1 Integrated model of self-regulated learning Once the SRL cycle is completed students who become proficient at the observation level enter the next level of competency attainment, emulation. Students who have not yet fully benefitted from observational learning repeat the cycles until the first level of SRL competency is reached. They may have to cope with experiences of failure and the teacher can slow the pace down to provide additional learning opportunities that will foster mastery experiences to build self-efficacy. By monitoring, supporting, and providing feedback through the SRL cycles and levels of competency, teachers can determine which students need more time and adapt instruction to the needs of the students (Schunk & Zimmerman, 1998). The integrated model increases the focus on comparing one’s performance to self-set goals by encouraging individual tracking of performance with metacognitive monitoring of progress towards the learning goals. The established criteria of standards at the onset of a task is critical to directing students’ attention to specific behaviors through self-monitoring and self-evaluation. Kitsantas and Dabbagh (2011) observed that the cycle of SRL promotes individual empowerment partially because it reinforces the beliefs the individual holds in his or her ability to effectively control aspects of a learning experience towards a desired outcome. Effective learners require SRL strategies in order to influence what they want to learn and how they want to learn it in the context of a standards-based learning environment. Bandura’s (1997) emphasis on students becoming self-directed learners requires educators to facilitate flexibility, provide an understanding of the academic tasks, and give opportunities for learners to increase their capability to selfregulate towards goal attainment. Standards-based education provides an opportunity for teachers to design instruction that can deepen cognitive processing and increase self-efficacy (Clark, 2012). Self-efficacious individuals believe in their capability to perform designated tasks (Bandura, 1997; see Chen & Bembenutty, 2018/this volume). A significant amount of research has been conducted on the role of self-efficacy in teaching and learning environments and its link to academic achievement (see Usher & Schunk, 2018/this volume). With increased self-efficacy beliefs, students have an opportunity to demonstrate learning that is consistent with what was taught, but on their own. Selfefficacy is dynamic, meaning that it is continually susceptible to change (Schunk & DiBenedetto, 2015a, 2015b). Linking SRL with the current academic standards provides educators and researchers with opportunities to create learning environments that promote self-efficacy. Students’ ability to exercise environmental control and manage social interactions depends on the level of self-belief in their capabilities. The ultimate goal of academic standards is to prepare learners to become more self-efficacious so that they can attempt challenging material and demonstrate competency across disciplines. In order to reach their academic goals, students must be able to adapt successfully to new and more complex learning tasks while maintaining self-control of behaviors, thoughts, and actions. Research Evidence Linking Standards to Self-Regulated Learning Standards-based education, in many ways, is best achieved when students can effectively self-regulate. Historically, models of self-regulation have included some type of feedback loop during learning (Carver & Scheier, 2011; Zimmerman, 1989, 2000). This loop refers to the cyclical processes by which students monitor the effectiveness of their learning methods or strategies and, in response to feedback, make adjustments in their behavior (Zimmerman, 2000). Comparison to a standard or expected outcome is the only way an evaluation can be made to determine if the preset goals are being met (Cleary & Zimmerman, 2012; Winne & Hadwin, 1998). When feedback indicates a discrepancy between the selected standard and actual behaviors, adjustments are made in an attempt to modify specific behaviors to meet the standard. In this framework, learning is a continual process of establishing goals and adjusting patterns of behavior to match those goals more closely by using feedback that can come from an external source, such as a teacher or an internal source through self-monitoring. Self-reactions to feedback require attending to and tracking specific aspects of performance through self-monitoring, selfrecording, self-observation, and self-evaluation. However, a self-regulatory cycle is not completed unless the learner applies feedback during future learning efforts (see Cleary & Callan, 2018/this volume).
Research Evidence Linking Standards to Feedback The intention of feedback at any level should be to empower students to become self-regulated learners. In academic settings, feedback is information about how the students’ present states of learning and performance are related to goals and standards (Ivanic, Clark, & Rimmershaw, 2000; Nicol & Macfarlane-Dick, 2006). However, how students perceive and interpret feedback and how teachers intend feedback to be taken often leads to miscommunication due to unclear task specifications. Students cannot convert feedback statements into actions for improvement unless they have a working knowledge of the basic task requirements and the criteria by which their performance is judged. Teachers are often misinformed about their students’ prior knowledge and make assumptions that lead to setting the standard too high and inhibiting the development of a skill (Sadler, 2010). Labuhn, Zimmerman, and Hasselhorn (2010) explored the effects of self-evaluative standards and feedback on performance in mathematics problem solving and relevant subprocesses of SRL. Participants for the study were 90 fifth grade students randomly assigned to experimental and control groups. The experimental conditions were based on three types of self-evaluative standards (mastery learning, social comparison, and no externally set standards), and three types of feedback (individual, social comparison, and no feedback). Their results indicated that feedback positively influences self-regulatory processes in both social and individual contexts. Furthermore, students indicated the use of a standard to evaluate their performance. The researchers indicate that in the absence of externally set standards, self-set standards are employed, which might be less effective than externally set standards, and recommend clear communication of standards to students, and realistically achievable goals with a strong sensitivity to minor skill improvement. Zimmerman (2013) stressed the influential role of feedback as the means by which self-regulated learners have their attention drawn to components of the social and physical environments, the results of their actions, and the thinking processes about a given task. Consistent and constructive feedback provides the learner with opportunities to adapt their performance during the completion of a difficult task rather than to persist towards an unsuccessful completion of the task. To individually compare performance, the self-regulated teacher provides corrective feedback by focusing on standards that facilitate SRL and provide criteria for students. In addition, consistent referencing to the indicators of successful performance through modeling, reteaching, and leading students to correct answers can increase students’ motivation to complete the task. In addition, feedback that leads to students’ self-evaluation and correction increases self-efficacy (Pape, Bell, & Yetkin-Özdemir, 2013). Black and William (1998) conducted a meta-analysis of 250 studies spanning all educational sectors and focusing on real teaching situations. Their findings indicate that effective formative feedback produces improved learning and achievement across all content areas, knowledge, skill types, and levels of education. In addition, other studies demonstrate that formative assessment feedback has a profound influence on how students engage with and enact the processes of learning (Gibbs, 2014; Hattie & Timperley, 2007). Formative feedback influences the cognitive, behavioral, and affective domains of student learning: it provides a point of engagement through which it is possible to stimulate transformation within students’ learning, learning processes, behaviors, and motivation. Sadler (1989) identified three conditions in which students would benefit from feedback in academic tasks. First, students need to know what good performance is and therefore possess a concept of the target goal or standard. Second, students need to know how current actions relate to a performance that indicates successful progress towards the expected outcome or standard. Third, students need to know how to make necessary adjustments to current performance to come closer to the expected outcome or standard. Significant to Sadler’s argument is the comparison of student performance with a standard in order to obtain feedback from internal or external sources positioning formative assessment within a model of self-regulation.
Several researchers have supported formative assessment and feedback as important to models of SRL (Clark, 2012; Nicol & Macfarlane-Dick, 2006). Amongst other factors, the process requires specific targets, criteria, standards, and other external reference points in order for students to set goals and connect feedback to specific behaviors. In order for students to develop the self-regulation skills needed to prepare them for learning outside the classroom and throughout life, formative assessment cannot be exclusively the teacher’s responsibility (Boud, 2000). From a social cognitive perspective of self-regulation, students’ academic competence develops initially from social sources of academic skill, such as the teacher, and subsequently shifts to individual control (Schunk & Zimmerman, 1998). Therefore, the feedback loop in the SRL cycle generates formative assessments, and as students develop into self-regulated learners their ability to fully participate in meaningful dialogue with their teachers about personal goal progress will increase significantly. One requirement for feedback to be effective in facilitating SRL is keeping students well informed with statements that describe tasks to be attempted and the assessment criteria for achievement in a particular area of study. Statements in the form of goals derive from standards and guide the process necessary for starting the effective feedback flow that enables SRL (Rust, Price, & O’Donovan, 2003). Butler and Winne (1995) proposed a model of self-regulation in which they stressed the importance of feedback as a catalyst in every activity, stimulating students’ engagement in SRL. Additionally, Nicol and MacfarlaneDick (2006) presented a self-regulation model that addresses how feedback actually promotes self-regulation from both external and internal sources. In their model, external feedback includes input from peers’ or teachers’ comments either verbally or in written progress reports. The purpose of internal feedback is to provide individuals with ongoing self-monitoring and self-evaluation that could increase an awareness of outcomes and progress towards goal attainment, resulting in the development of an internal self-regulatory process (Chung & Yuen, 2011). To facilitate SRL teachers need to consider the impact of external feedback on the process of self-regulation, specifically encouraging the learner to consistently compare performance to standards and goals, not the progress of peers. Nicol and Macfarlane-Dick (2006) suggest that feedback should include clarifying to the learner what good performance is, facilitating self-assessment by the learner, encouraging teacher and peer dialogue, and encouraging positive motivation. Teachers’ guidance could help students set goals, make good use of learning strategies and resources, and sustain motivation. In turn, learners are encouraged to be actively involved in monitoring and regulating their own performance in terms of set goals and the strategies used to reach their goals. Research Evidence Linking Standards to Academic Goals In today’s academic settings, the teacher directs students’ attention to selected standards from which goals will be set for a specific task. When standards are presented with realistic goals, motivation to engage in the task is high because students are able to make comparisons between the standard and their goal progress (Zimmerman, Schunk, & DiBenedetto, 2015). Adjustments can be made to the language of standards to increase self-efficacy by translating the standard into proximal and achievable goals that can be monitored and assessed by both teacher and student. Goals provide standards against which people can compare their present performances (Bandura, 1986). When standards include performance indicators from which students can measure their progress students can better measure their self-efficacy for a given task. Consideration of how students approach and reach goals requires a sense of self-efficacy for learning (Schunk & Swarts, 1993). Schunk (1996) studied children’s self-evaluation of their learning with 44 fourth graders who were average achievers in mathematics. Linking self-evaluations to learning goals, students were encouraged to learn to solve the problems and periodically evaluate their capabilities when compared to their requirements of the task. Findings indicated that having children periodically evaluate their capabilities makes it clear that they have made progress in learning, and this perception strengthens selfefficacy and keeps students working productively. Self-evaluation is especially important under performance goal
conditions. To the extent that learning goals or standards produce a long-term focus on skill improvement, periodic self-evaluation should highlight to students that they are making progress in skill acquisition. Monitoring the effectiveness of students’ learning methods and strategies in reaching their goals has been found to enhance the effects of goal setting, self-efficacy beliefs, and self-reactions. Reactions to this feedback included changes in skilled behavior and self-perceptions. Zimmerman and Kitsantas (1997) studied the effects of goal setting and self-monitoring during self-regulated practice on the acquisition of a complex motoric skill with 90 high school girls during dart-throwing exercises. They found that the girls who shifted goals developmentally from process to outcome goals surpassed classmates who adhered to only process goals and who, in turn, exceeded classmates who used only outcome goals in posttest dart-throwing skill, self-reactions, self-efficacy perceptions, and intrinsic interest in the game. Winne (2001) emphasized the importance of precision when making comparisons generated from monitoring between the outcome and the actual standard. The more conditions added to the rules to classify when a standard is met, the more precisely learners will match actions with task requirements. Successful learning outcomes are products of learners acquiring the ability to reach goals more effectively (Winne, 1995). Winne and Hadwin (1998) theorized that learning occurs in four basic phases: task definition, goal setting and planning, studying tactics, and adaptations to meta-cognition (Greene & Azevedo, 2007). Within each phase, information processes construct standards-based information products that include the qualities of the predicted or actual goal or standard. Although Winne and Hadwin (1998) identified standards as an internal interpretation of task conditions from an external source such as the teacher, this model stresses the importance of having an external standard from beyond the teacher’s gauge by which to measure growth and learning from a global perspective. Research Evidence Linking Standards to Formative Assessment Educators specifically generate formative assessments in order to improve or accelerate learning (Sadler, 1989). Standards have emphasized the need for formative assessment, a practice strongly advocated by self-regulation theorists. Clark (2012) suggests that through the use of formative assessment students are given feedback that can be compared with the information gained from self-monitoring and, as a result, become a catalyst for each iteration of the SRL cycle. Standards guide the teacher in selecting tasks and setting the criteria to begin the feedback cycle. Once the cycle is initiated, the teacher provides external feedback prompting the students to generate their own feedback through self-monitoring and self-evaluation of their goal progress. Consistent comparison to the standard guides the process until the task is complete to the satisfaction of both the student and the teacher. Both self-assessment and self-evaluation strategies along with the instructor’s evaluations provide formative information for the learner to reflect on and apply to the next task. The strategic purpose of formative assessment is to promote an autonomous use of both cognitive and social strategies required for academic success in SRL (Clark, 2011). The process is considered to be bidirectional in that feedback provides learners with information on how to more accurately perform tasks while providing teachers with information that can guide instruction. Teachers are the implementers of the standards, making decisions every day regarding classroom pedagogy while assessing individual students’ prior knowledge, learning development, and learning differences. The implementation of standards is the foundation to ensuring that all students, regardless of where they live and attend school, will be prepared for success in postsecondary education and the workforce. Raising teachers’ awareness of the impact their self-regulatory instructional practices have on their students is critical to creating learning environments where self-regulatory processes empower learners to achieve high levels of personal, academic, and professional outcomes (Butler, Schnellert, & Perry, 2017; Winne & Hadwin, 2008; Zimmerman, 2013). Instruction in SRL serves as a framework for metacognitive knowledge and skill acquisition that in turn builds students who are not only college and career ready but lifelong learners (White & DiBenedetto, 2015). There are standards, pedagogical approaches, curriculum choices, and student learning processes evident in learning environments that support self-regulation and are helping teachers and teacher educators transition